<p><strong>August 1, 2023 – Tokyo Japan,</strong> <a href="https://leapmind.io/en/">LeapMind Co., Ltd.,</a> a leading creator of the standard in edge artificial intelligence (AI) announced today that it has achieved an industry-leading power efficiency of 107.8 TOPS/W*1, 2 with “Efficiera”, an ultra-low power AI accelerator IP using the company's core technology, “Extremely low bit quantization”. When comparing this to similar devices in today’s market, 1 ~ 5 TOPS/W in the case of GPUs and about 20 TOPS/W for AI accelerators for edge devices, this means that Efficiera performance would be approximately 5~100 times higher than these devices at the same power consumption.</p>
<h1 id="hardware-challenges-with-increasing-size-of-ai-tasks">Hardware challenges with increasing size of AI tasks</h1>
<p>In recent years, as the execution of AI tasks has become practical on various devices, AI tasks have become larger and more complex year by year. According to the analysis by OpenAI*3, the amount of AI computation used has dramatically increased at a rate of 2x in 3-4 months since 2012 (Moore's Law is 2x in 2 years), and it is worth preparing for the systems far beyond current capabilities. The analysis also notes that the processing of such AI tasks has been driven by custom hardware such as GPUs and/or more chips in parallel. This could indicate two technical challenges for the practical application of edge AI. First, as AI tasks become increasingly complex and larger in scale, the computational performance required during AI training and inference is much higher than before. The second is the challenge regarding the power consumption of devices with higher-performance AI chips processing complex AI tasks. The power available for an AI chip to process AI tasks stably in smartphones and AR glasses is less than 1W and around 25W for autonomous driving, where advanced AI processing is required. This means, edge AI also requires high power efficiency.（Figure 1)</p>
<p><img src="/images/posts/news/230801/01.png" alt="Diagram of AI tasks and performance requirements">
Figure1　AI task and required performance</p>
<p><img src="/images/posts/news/230801/02-en.png" alt="Power Efficiency Comparison Chart">
Reference：Power efficiency comparison (Based on in-house research)</p>
<h1 id="low-bit-quantization-technology-and-a-solution-for-enabling-edge-ai-with-efficiera">Low-bit quantization technology, and a solution for enabling edge AI with “Efficiera”</h1>
<p>Deep learning-based computations are usually performed with 32 bit floating point (FP32) to ensure accuracy. However, this method involves repeated matrix operations (sum-of-products operations) and requires a lot of computational resources, and several important requirements must be met in both software and hardware to perform AI tasks on edge devices, where memory and power are limited and real-time execution is required. The followings are some of the key requirements to be met.
First of all, software requirements include lightweight models, model accuracy comparable to FP32, and model conversion to maximize the efficiency of AI accelerator execution. Second, hardware requirements include low power consumption, high performance for executing various AI tasks, less memory bandwidth, and less memory usage.</p>
<p>To enable edge AI, all of these requirements must be addressed simultaneously, but a general approach is to first use a technique called quantization to lightweight the model. Lightweight 8 bit quantization is commonly used for AI models running on edge devices such as smartphones, but even with this, the AI tasks viable for implementation are still limited to some tasks such as object detection and image processing of static images, from the standpoint of power consumption and heat dissipation. In order to implement various AI tasks, the challenge is that the power efficiency and hardware performance to support many of AI tasks are not sufficient for the scale of those tasks.</p>
<p>In response, LeapMind aims to enable practical AI on various devices, and to solve both software and hardware requirements with our strength, “Extremely low bit quantization” technology. As a part of our effortson the software side, we use this “Extremely low bit quantization” to reduce the size of the model, achieving an ultra-low bit quantization of 1-bit weight x 2-bit activation, and our patented proprietary quantization technology has brought us success in low-bit quantization with minimal degradation in accuracy. In addition, a model converter dedicated to "Efficiera" optimizes computing efficiency and memory transfer to maximize hardware execution efficiency. Furthermore, we have been developing the AI accelerator "Efficiera" as hardware for the efficient execution of Extremely low bit quantization models. Our Extremely low bit quantization technology enables to perform matrix operations with simple logic operations, thus providing high arithmetic efficiency in a small circuit area. Since the DRAM memory transfer data during inference is 1 or 2 bits, the data size is equivalent to that of the data compression, reducing memory usage and bandwidth. By using Extremely low bit quantization technology, it is now possible to improve power efficiency, arithmetic efficiency, and area efficiency while minimizing the accuracy degradation caused by quantization.</p>
<p>Matsuda, Soichi, Chief Executive Officer of LeapMind, says  “As one of the few companies in the world that specialize in research and development of quantization technology as a method of AI technology and in the design of semiconductors for AI accelerators developed, we have been engaged in research and development for many years through joint development with major companies with the aim of achieving the practical application of AI. The balancing of high power efficiency and AI task processing performance with AI accelerators is essential for the implementation of AI in society. And today, we have achieved a power efficiency of 107.8 TOPS/W, far exceeding that of conventional GPUs and NPUs in terms of power efficiency. I believe that we have achieved an important milestone in our efforts to bring AI to the public more broadly.  We will continue to strive to solve our customers' problems and pursue research and development to contribute to the evolution of AI technology”.</p>
<p><strong>Please refer to LeapMind website below for the details of Efficiera.</strong>
<a href="https://leapmind.io/en/products/efficiera-ip/">https://leapmind.io/en/products/efficiera-ip/</a></p>
<p><strong>Appendix</strong>
*1  Estimated values are based on logic synthesis results using Cadence Genus Synthesis Solution. Conditions are as follows.
・Calculated based on IP stand-alone (Not including power of other subsystems such as DRAMs, main bus processors, etc.)
・Process: 7nm
・Clock frequency：533MHz
・Power supply voltage: Center
・Temperature: 25℃
・MAC utilization: 75.8%
*2  TOPS/W: Processing performance per watt（TOPS: tera operations per second）This indicates power efficiency. The higher this number, the greater the amount of computation to be processed with low-power consumption.
*3 <a href="https://openai.com/research/ai-and-compute">https://openai.com/research/ai-and-compute</a></p>
<h2 id="about-leapmind-inc">About LeapMind Inc.</h2>
<p>Founded in 2012, LeapMind has been providing solutions mainly to the consumer electronics, automotive, and manufacturing industries, as one of the few companies in the world that provide “Extremely low bit quantization”  technology to downsize AI and proprietary AI accelerator semiconductor design, under the corporate mission of "To create innovative devices with machine learning and make them available everywhere.” In order to provide foundational technologies to enable the next generation of information devices, we are committed to the development of both the software and hardware necessary to create such devices.LeapMind's business is highly acclaimed and has raised a cumulative total of 4.99 billion yen in funding (as of June 2023). For more information, please visit corporate website (<a href="https://leapmind.io/en/">https://leapmind.io/en/</a>)</p>
<p>Head office: Shibuya Dogenzaka Sky Building 3F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL：<a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>*LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
*The information in the press release is current as of the date of announcement.</p>
<p>Media Contact
Marketing and Communication Group, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>

LeapMind’s Ultra Low-Power AI accelerator IP “Efficiera”
Achieved industry-leading power efficiency of 107.8 TOPS/W

<p>January 31, 2023 - LeapMind Inc. (Shibuya-ku, Tokyo; CEO: Soichi Matsuda), a creator of the standard in edge AI, has celebrated its 10th anniversary of founding last December, and is pleased to announce that Mr. Takayoshi USHIKU has been appointed as a Technical Advisor since January, with the aim of further penetrating deep learning technology into the market.</p>
<h2 id="background">Background</h2>
<p>Since founded in 2012, LeapMind has been developing both software and hardware by using deep learning quantization technology for aiming machine learning into practical use. After 10 years since its founding, we’re seeing that LeapMind’s technology is steadily approaching the end-products that benefit people's lives; however, at the same time, feel that there are still more rooms to expand the understanding of deep learning technology usage, and needs more research and development and activities to raise awareness.
Mr.Ushiku is a computer vision and natural language processing researcher and has been dedicated to development of machine learning and related areas; written numerous papers and has been cited in international papers more than 5,000 times*1, currently teaching at multiple universities, serves as a member of many expert committees, including The Japanese Society for Artificial Intelligence. Also, since 2018, he has been active as a Principal Investigator at OMRON SINIC X Corporation since its establishment, to connect social issues to next-generation innovative technologies.
As a technical advisor, Mr. Ushiku will provide advice on the company's technology strategy for the next 10 years, and will also be involved in supporting the strengthening of our R&#x26;D organization.
Combining Mr. Ushiku's research with our technology related to image processing AI, we will strengthen technology research and development, accelerate commercialization, and promote the realization of social implementation of edge AI.</p>
<p>*1 : Google Scholar search result</p>
<p><img src="/images/posts/news/7912/5e0a0e5e-ushiku_insert.png" alt="Board member of LeapMind Inc." title="Board member of LeapMind Inc.">
(Board member of LeapMind Inc. From right CEO Matsuda, Mr. Ushiku, CTO Tokunaga)</p>
<h2 id="career-of-takayoshi-ushiku">Career of Takayoshi USHIKU</h2>
<ul>
<li>2013 Research Fellow, Japan Society for the Promotion of Science, Microsoft Research Redmond Intern</li>
<li>2014 PhD, Graduate School of Information Science and Technology, The University of Tokyo
Research Scientist, NTT Communication Science Laboratories</li>
<li>Research Scientist, NTT Communication Science Laboratories</li>
<li>2016 Associate Professor, Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo</li>
<li>2018 Principal Investigator, OMRON SINIC X Corporation</li>
<li>2019 Chief Research Officer, Ridge-i Co., Ltd.</li>
<li>2022 Chief Executive Officer, Nine Bulls, LLC</li>
</ul>
<p>The research interests lie in cross-media understanding through machine learning, mainly for computer vision and natural language processing. His research results have received various awards; ACM Multimedia Grand Challenge Special Prize in 2011, ACM Multimedia Open Source Software Competition Honorable Mention in 2017, and NVIDIA Pioneering Research Awards in 2017 and 2018.</p>
<h2 id="comment-from-technical-advisor-mr-yopshitaka-ushiku">Comment from Technical Advisor Mr. Yopshitaka Ushiku</h2>
<p>I have thought for a long time that LeapMind is a standard-bearer in efforts to popularize neural networks on embedded devices in Japan. When it comes to enriching people's lives, simply making PCs and smartphones smarter is not enough, and it is a very difficult task to make even smarter those embedded devices which are already useful. On the other hand, because it is difficult, the meaning of performing neural network processing on embedded devices is extremely high, and the idea of ​​”using embedded technology to spread it widely and enrich people's lives” is quite important and meaningful.
It may sound grandiose but I want to support technological innovation with the big ambition of “run by the embedding on a large-scale base model, putting a huge network on the SoC which was thought to be “absolutely unworkable with embedding.” 　</p>
<h2 id="comment-from-cto-hiroyuki-tokunaga">Comment from CTO Hiroyuki Tokunaga</h2>
<p>LeapMind is currently developing an accelerator for neural networks, but the future of the highly intelligent computers would not be a simple extension of today's technology, and at some point it will require some kind of technological leap. In order to respond to such technological evolution, I am very pleased to have Mr. Ushiku as an advisor who has been involved in leading-edge research and development. His participation is definitely a great reassurance to draw our future picture.</p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate philosophy of "bringing new devices that use machine learning to the world". Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, centered in manufacturing including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware.</p>
<p>Head office: Shibuya Dogenzaka Sky Building 3F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL：<a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>*LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.</p>

【Press release】Yoshitaka Ushiku appointed as Technical Advisor

<p>September 16, 2022 - LeapMind Inc. (Shibuya-ku, Tokyo; CEO: Soichi Matsuda), a creator of the standard in edge AI have announced that it has been granted their first overseas patent in Taiwan. The patent is awarded for the invention related to neural network circuits, edge devices and neural network operation method, which is part of our core technology, extremely low-bit quantization used for the flagship product, ultra-low power AI interface accelerator IP “Efficiera®”. It is a pursued to the utmost limit technology of the weight reduction method of deep learning model and has already been granted in Japan in June, 2021.</p>
<p><strong>Patent number:</strong> No. I773245</p>
<p><strong>Date of patent registration:</strong> August 1st, 2022</p>
<p><strong>Title of the invention:</strong> Neural Network Circuits, Edge Devices and Neural Network Operation Methods</p>
<p><strong>Patentee:</strong> LeapMind Inc.</p>
<p>LeapMind has been working to acquire patents on ultra-low quantization technology and Efficiera® in Japan and overseas, and has obtained seven patents in Japan, including this. LeapMind will continuously be committed to strive in advanced technology development by acquiring patents and to accelerate the social implementation of AI.</p>
<p><strong>List of LeapMind’s patent in Japan</strong>
<img src="/images/posts/en/news/7900/9bc7c488-eng.png" alt="List of patents granted by LeapMind to date." title="List of patents granted by LeapMind to date."></p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate philosophy of "bringing new devices that use machine learning to the world". Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, centered in manufacturing including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware.</p>
<p>Head office: Shibuya Dogenzaka Sky Building 3F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL:<a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>*LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.</p>
<p>Media Contact
Marketing and Communication Group, Public Relations Officer, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>

LeapMind 1st-patents in Taiwan for its low bit quantization technology

<h2 id="focusing-to-visual-inspection-edge-ai-enables-fast-and-secured-manufacturing-dxtraining-with-only-a-few-normal-data-all-ai-operations-completes-on-edge-devices">Focusing to visual inspection, edge AI enables fast and secured manufacturing DX
‐Training with only a few normal data, all AI operations completes on edge devices‐</h2>
<p>May 25, 2022– Tokyo Japan, LeapMind Co., Ltd., today announced a new Deep Learning model “Efficiera Anomaly Detection model” based on its ultra-low power consumption AI inference accelerator IP "Efficiera®" and will be available from June, 2022. This specializes in visual inspection for the manufacturing industry and contributes to reducing security risk and labor shortage at the manufacturing site.</p>
<p><img src="/images/posts/en/news/7683/68cfde0b-ad-imageeng.png" alt="Image of the use of the Efficiera Anomaly Detection Model." title="Image of the use of the Efficiera Anomaly Detection Model."></p>
<h2 id="features-of-efficiera-anomaly-detection-model">Features of “Efficiera Anomaly Detection model”</h2>
<h3 id="training-completes-with-only-normal-data">•Training completes with only normal data</h3>
<p>Annotation work is not required which is often outsourced, no necessity of concluding non-disclosure agreement.
Avoids the need of large amount of defective data, and training completes just in a few seconds which enabling rapid introduction of AI to the manufacturing site</p>
<h3 id="completes-both-inference-and-training-on-small-edge-device-with-fpga">•Completes both inference and training on small edge device with FPGA</h3>
<p>No need of send image data outside the company, the risk of information leakage is reduced, and complicated non-disclosure agreements are not required which could lead to labor savings
Can be used in environment without network</p>
<h3 id="re-training-can-be-done-without-an-ai-engineer">•Re-training can be done without an AI engineer</h3>
<p>Visualization of abnormal areas by heat map.</p>
<p>AI training results can be saved and reloaded (redo and version management) easily on site which makes it easy to use to those companies that have difficulty in securing IT human resources.</p>
<p>The needs of automation in the manufacturing industry has been growing due to the chronic understaffing problem. AI usage is brought to the attention for visual inspection for quality check, but the challenges are the shortage of IT human resources and high cost of equipment. Other challenges are collecting defective datas for AI training and the complication of high-mix low-volume production due to the diversification demand and this may be cases where the support of AI engineers is required for AI retraining due to a change in the inspection target. Efforts to deal with these issues are also becoming a hurdle to adoption.</p>
<p>LeapMind has been developing an AI inference accelerator IP “Efficiera®” which can embed deep learning technology into various edge devices to popularize the devices with deep learning technology to the world to make people’s lives convenient. A newly developed “Effciera Anomaly Detection model” lowers the hurdle to introduce AI to visual inspection by using its own unique “Extremely low bit quantization” technology. The key feature of this deep learning model is that both inference and training can be completed on small edge devices with FPGA. Comparing the case using cloud AI, it has an advantage that there is no need to send inspection target images from the device itself which reduces the risk of data breach, contributes to reduce broadband infrastructure and cloud service usage fee. Also, compared to the case of using on-premises GPU server, it resolves concerns of the equipment cost and the installation space. The training can be completed in a few seconds only with a few dozens of normal product images and no necessity of collecting defective datas. This requires no annotation work and makes it possible to detect unexpected abnormalities and lowers the risk of only detecting trained defect targets. This model visualizes the part judged by AI as abnormal with a heating map and retraining operation is very simple so that it makes on-site adjustment easy without the support of AI engineers even on high-mix low-volume production lines. Since the training result is saved in a few MB of data and can be used for inference later, it is easy to redo the wrong training. This will help boost DX in the manufacturing industry even for companies that have difficulty in securing IT human resources or companies that have manufacturing bases in rural areas where broadband networks cannot be used.</p>
<p><img src="/images/posts/news/7679/2ef74f6d-.png" alt="visual inspection using the &#x22;Efficiera&#x22; anomaly detection model.">
Image : visual inspection using the "Efficiera" anomaly detection model. Abnormal parts of defective products can be seen at a glance by visualization with a heat map</p>
<p>Based on our vision “To provide key technologies to bring next-generation information devices into reality”, we will continue to strive to the realization of more convenient lives for people by realizing edge AI into practical use.</p>
<h2 id="about-efficiera">About Efficiera®</h2>
<p>Efficiera is an ultra-low power AI inference accelerator IP specialized for CNN inference processing that runs as a circuit on FPGA or ASIC devices. The "extremely low bit quantization" technology minimizes the number of quantization bits to 1 - 2 bits, maximizing the power and area efficiency of convolution, which accounts for most of the inference processing, without the need for advanced semiconductor manufacturing processes or special cell libraries. By using this product, deep learning functions can be incorporated into a variety of edge devices, including consumer electronics such as home appliances, industrial equipment such as construction machinery, surveillance cameras, broadcasting equipment, as well as small machines and robots that are constrained by power, cost, and heat dissipation, which has been technically difficult in the past. LeapMind provides three Deep Learning models - Efficiera Object Detection model, Efficiera Noise Reduction model and Efficiera Anomaly Detection model. Visit product website at <a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a></p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate mission "To create innovative devices with machine learning and make them available everywhere." Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, many of which are centered in manufacturing, including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware</p>
<p>Head office: Shibuya Dogenzaka Sky Building 3F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL: <a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>*LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
*The information in the press release is current as of the date of announcement.</p>
<p>Media Contact
Efficiera Division, Marketing and Communication Team, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a>　</p>

【Press release】LeapMind announces “Efficiera® Anomaly Detection model”

<h2 id="adopted-on-maxell-frontiers-new-image-recognition-solution-ixam-vision-series">Adopted on Maxell Frontier’s new image recognition solution "iXAM Vision series"</h2>
<p>February 22, 2022– Tokyo Japan, LeapMind Co., Ltd. (Shibuya, Tokyo; CEO: Soichi Matsuda), a leading supplier of the standard in edge artificial intelligence (AI), today announced that have concluded a commercial license agreement of LeapMind’s ultra-low power AI inference accelerator IP "Efficiera" with Maxell Frontier Co., Ltd. (Yokohama, Kanagawa; President: Hiroyuki Ota), the group company of Maxell, Ltd.
Efficiera has been adopted as a technology to enhance AI processing performance in their new image recognition solutions "iXAM Vision series" announced by Maxell Frontier today and scheduled to be launched in October 2022.</p>
<p>This collaboration of LeapMind’s "Extremely low bit quantization," which supports the application of deep learning technology on edge devices with restricted power and space, and Maxell Frontier which has the proven track record of over 30 years will boost the AI performance into the next stage of their image recognition systems.
Together with “Efficiera” IP, LeapMind is providing Efficiera deep learning models “Object detection” and “Anomaly Detection”. The big features of Efficiera are space-saving, the compactness, and of the deep learning model are easy installation to edge devices and re-training. With those features, it is possible to shorten the development time of AI functioned edge devices.
LeapMind will continue to strive to popularize edge AI and aim to solve social issues through deep learning technology.</p>
<h2 id="about-ixam-vision-series-of-maxell-frontier">About “iXAM Vision series” of Maxell Frontier</h2>
<p>iXAM series lines-up two types of unit products, 1) “iXAM Vision Engine” - the intelligent image processing unit, 2) “iXAM Vision Sensor” - the intelligent camera, and “iXAM Vision Meister” - the AI image inspection application development tool, which runs on the unit products. These are scheduled to be launched in October 2022.
Maxell Frontier’s product release:<a href="https://ssl4.eir-parts.net/doc/6810/ir_material21/177469/00.pdf">https://ssl4.eir-parts.net/doc/6810/ir_material21/177469/00.pdf (Japanese)</a></p>
<h2 id="about-efficiera">About Efficiera</h2>
<p>Efficiera is an ultra-low power AI inference accelerator IP specialized for CNN inference processing that runs as a circuit on FPGA or ASIC devices. The "extremely low bit quantization" technology minimizes the number of quantization bits to 1 - 2 bits, maximizing the power and area efficiency of convolution, which accounts for most of the inference processing, without the need for advanced semiconductor manufacturing processes or special cell libraries. By using this product, deep learning functions can be incorporated into a variety of edge devices, including consumer electronics such as home appliances, industrial equipment such as construction machinery, surveillance cameras, broadcasting equipment, as well as small machines and robots that are constrained by power, cost, and heat dissipation, which has been technically difficult in the past. Visit product website at <a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a></p>
<h2 id="deep-learning-model-for-efficiera">Deep learning model for Efficiera</h2>
<p>The Efficiera deep learning model is a trained model that can be used on edge devices. LeapMind develops andprovides trained models of Object detection, Anomaly Detection and AI Image processing.</p>
<p>Object Detection model:
The position information of the detection target can be acquired from the image. Can be applied to a wide range of applications by combining with various logic of processing the position information.</p>
<p>Anomaly Detection model:
Realize the real time visual inspection on edge device by AI. Easily complete the AI training on edge devices even the format of the inspection target changes.</p>
<p>AI Image Processing model:
Operates in real time on edge devices to reduce the noise and improve the image quality of noisy images.</p>
<h2 id="about-maxcell-frontier">About Maxcell Frontier</h2>
<p>Head office: YBP North Square III, 134 Godo-cho, Hodogaya-ku, Yokohama, Kanagawa 240-0005
Representative: Hiroyuki Ota, President
Established: April 2020
URL:<a href="https://www.frontier.maxell.co.jp">https://www.frontier.maxell.co.jp</a></p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate mission "To create innovative devices with machine learning and make them available everywhere." Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, many of which are centered in manufacturing, including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware
Head office: Shibuya Dogenzaka Sky Building 3F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL: <a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>*LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
*The information in the press release is current as of the date of announcement.</p>
<p>Media Contact
Efficiera Division, Marketing and Communication Team, LeapMind Inc.
Phone: 813-6696-6267
Email:<a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>
<p>Product Contact
Efficiera Division, Business Development Team, LeapMind Inc.
Phone: 813-6696-6267
Email:<a href="mailto:business@leapmind.io">business@leapmind.io</a>
Product information:<a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a></p>

【Press Release】LeapMind announces license agreement for AI accelerator “Efficiera” with Maxell Frontier

<h2 id="achieving-high-quality-image-like-a-smartphone-with-edge-camera-by-ai-noise-reduction">Achieving high quality image like a smartphone with edge camera by AI noise reduction</h2>
<p>January 31, 2022– Tokyo Japan, LeapMind Co., Ltd., a leading creator of the standard in edge artificial intelligence (AI), today announced an AI image processing model which operates in real time on edge device. This AI image processing model uses the company's ultra-low power consumption AI inference accelerator IP "Efficiera" to operate in real time on edge devices to reduce the noise and improve the image quality of noisy images. This solves the challenges of performance and image quality accuracy that have been raised in the past, and achieved high performance, high image quality, and weight reduction.</p>
<h2 id="challenges">Challenges</h2>
<p>Performance challenge: difficult on edge device to operate in real-time with the high calculation cost on AI image processing
Image quality accuracy challenge: Extremely low bit quantization for AI image processing that inputs and outputs the images has degradation in image quality (reproduction of color and resolution, etc.).</p>
<h2 id="solutions-and-achievements">Solutions and achievements</h2>
<p>The company's extreme low bit quantization technology has made it lighter, and by combining it with the performance scalability of Efficiera v2, it became possible to operate in real time even with a video camera.
The company's Pixel embedding*¹ technology has achieved high image quality and image quality comparable to that of commonly used 32-bit floating point models. As a result, image quality can be improved by AI without the need for high-sensitivity sensors and large lenses, which are the high cost components. And high image quality like smartphones is possible by AI to those image quality oriented cameras, which makes industrial cameras (security cameras, inspections cameras, etc.) improve the accuracy of object recognition and inspection by improving the image quality even in low-light conditions or those video cameras which cannot take sufficient exposure time.</p>
<p>This model will be available as an evaluation version from February 2022. To obtain, please contact us at <a href="mailto:business@leapmind.io">business@leapmind.io</a>.</p>
<p>“As far as we searched, we are the world’s first in bringing this Image processing AI model into the product by low bit quantization technology. This model is a product that can be put into practical use only because of LeapMind focusing on both hardware and software development and we believe that it shows the new value of extremely low bit quantization.” says Hiroyuki Tokunaga, CTO of LeapMind.</p>
<p>*¹Pixel embedding: A method of encoding an input image into 2-bit data of multiple channels</p>
<h2 id="achieving-image-quality-comparable-to-the-32-bit-floating-point-model-with-the-ultra-small-quantization-12-bit-model">Achieving image quality comparable to the 32-bit floating point model with the ultra-small quantization (1,2 bit) model</h2>
<p>Input image Left: ISO51200, 1/800sec, F4.0, Right: ISO102400, 1/320sec, F8.0
<img src="/images/posts/news/7528/ca32098b-input.png" alt="Conventional images with noise." title="Conventional images with noise."></p>
<p>Output image: 32 bit floating point model
<img src="/images/posts/news/7528/4804c6b3-fp32.png" alt="Image of 32-bit floating-point model with slightly reduced noise." title="Image of 32-bit floating-point model with slightly reduced noise."></p>
<p><strong>Output image: LeapMind Extremely low bit quantization model</strong>
<img src="/images/posts/news/7528/ec6fe060-efficieranr.png" alt="Image of the LeapMind micro-quantisation model with reduced noise." title="Image of the LeapMind micro-quantisation model with reduced noise."></p>
<h2 id="features">Features</h2>
<ul>
<li>Raw-to-raw noise reduction model for raw input and raw output
　　- Minimum impact to Existing image processing pipeline due to Raw-to-raw</li>
<li>Deep learning-based noise reduction processing
　　- State-of-the-art NR algorithm optimized for embedded applications</li>
<li>Provides a retrainable trained model
　　- Optimize the model by retraining sensor-specific noise in addition to trained noise</li>
<li>Light weight processing for real-time operation
　　- Supports real-time operation with video cameras due to Efficiera v2's performance scalability</li>
</ul>
<p><img src="/images/posts/news/7528/b5a83b3a-nr_technical_image.png" alt="Deep learning-based noise reduction processing image." title="Deep learning-based noise reduction processing image."></p>
<h2 id="about-efficiera">About Efficiera</h2>
<p>Efficiera is an ultra-low power AI inference accelerator IP specialized for CNN inference processing that runs as a circuit on FPGA or ASIC devices. The "extremely low bit quantization" technology minimizes the number of quantization bits to 1 - 2 bits, maximizing the power and area efficiency of convolution, which accounts for most of the inference processing, without the need for advanced semiconductor manufacturing processes or special cell libraries. By using this product, deep learning functions can be incorporated into a variety of edge devices, including consumer electronics such as home appliances, industrial equipment such as construction machinery, surveillance cameras, broadcasting equipment, as well as small machines and robots that are constrained by power, cost, and heat dissipation, which has been technically difficult in the past. Visit product website at <a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a></p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate mission "To create innovative devices with machine learning and make them available everywhere." Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, many of which are centered in manufacturing, including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware</p>
<p>Head office: Shibuya Dogenzaka Sky Building 3F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL: <a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
The information in the press release is current as of the date of announcement.</p>
<p>Media Contact
Efficiera Division, Marketing and Communication Team, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>
<p>end</p>

【Press release】LeapMind Announces AI Image processing model which operates on edge devices

<p>Enhance product for wider-range application based on initial market introduction, results and evaluation</p>
<p>November 30, 2021 – Tokyo Japan, LeapMind Co., Ltd., a leading creator of the standard in edge artificial intelligence (AI), today announced version 2 (hereinafter: v2) of the ultra-low power AI inference accelerator IP Efficiera, scheduled to be available in December 2021. Efficiera is highly valued for its power saving, high performance, space saving and performance scalability features. Efficiera v2’s improved features and benefits expand the range of applications by providing wider coverage of performance with broader product availability while maintaining the circuit scale of the minimum configurations. These enhancements are based on learnings from the initial product introduction and market evaluation.</p>
<p>"Last year, we officially launched the commercial version of v1, and many companies evaluated Efficiera, and by the end of September 2021, we signed license agreements with eight domestic companies. Our corporate motto is concretely feeling that the "spreading to the world new devices that use machine learning " that we have set as our corporate philosophy is steadily progressing through the provision of v1. We will continue to strive to popularize AI through further technological innovation and product lineup expansion." said Soichi Matsuda, CEO of LeapMind.</p>
<p>Efficiera is an ultra-low power AI inference accelerator IP specialized for convolutional neural network (CNN) inference processing that runs as a circuit on FPGA or application-specific integrated circuit (ASIC) devices. The ultra-small quantization technology minimizes the number of quantization bits to 1 - 2 bits, maximizing the power and area efficiency of convolution that accounts for most of inference processing without the need for advanced semiconductor manufacturing processes or special cell libraries. By using this product, deep learning functions can be incorporated into a variety of edge devices, including consumer electronics such as home appliances, industrial equipment such as construction machinery, surveillance cameras, broadcasting equipment, as well as small machines and robots that are constrained by power, cost, and thermals — all of which have been technically difficult in the past.</p>
<p>"Since the official release of v1, we have aimed to develop the world's most power efficient DNN accelerator. We have strengthened the design and verification method and development process. We developed v2 so that it can respond to adoption in ASICs and application-specific standard products (ASSPs). We are also developing an inference learning model on the deep learning side to maximize the benefits of ultra-small quantization technology. The greatest strength of LeapMind is that we can provide a technique to master these two wheels,” said Dr. Hiroyuki Tokunaga, Director and CTO of LeapMind.</p>
<p>By improving design and verification methodology and reviewing the development process, product quality can be applied not only to FPGA but also to ASIC and ASSP. LeapMind starts to provide a model development environment, Network Development Kit (NDK) which enables users to develop deep learning models for Efficiera, which has not been done previously.</p>
<h2 id="other-notable-specifications-and-features-of-efficiera-v2">Other Notable Specifications and Features of Efficiera V2</h2>
<p>Concept of Efficiera v2
<img src="/images/posts/news/7432/0be4b840-efficiera2%E3%82%B3%E3%83%B3%E3%82%BB%E3%83%97%E3%83%88%E5%9B%B3-660x371.png" alt="Concept of Efficiera v2" title="Concept of Efficiera v2"></p>
<h3 id="hardware-features">Hardware features</h3>
<ul>
<li>Performance scalability up to 48 times by multiplexing MAC array + multi-core.</li>
<li>v2 allows you to triple the number of MAC arrays in the Convolution pipeline to v1 (x1, x4 selectable), and expands performance scalability by allowing you to select up to 4 cores.</li>
<li>Hardware execution of Skip connection and Pixel Embedding in addition to convolution and quantization.</li>
<li>Resources equivalent to Efficiera v1 were used with the same configuration and analyzed to understand execution time and assess if additional hardware functions are required.</li>
</ul>
<h3 id="integration-into-soc">Integration into SoC</h3>
<ul>
<li>AMBA AXI interface</li>
<li>AMBA AXI continued to be used as the interface with the external and the interface when viewed as a black box is the same as before however it is now easier to migrate from the existing design.</li>
<li>Single clock domain</li>
</ul>
<h3 id="target-frequency-in-fpga">Target frequency in FPGA</h3>
<ul>
<li>The operating frequency of the FPGA is the same as before and although it depends on the device, about 150 to 250 MHz can be expected.</li>
<li>256 GOP/s @ 125MHz (1 core)</li>
<li>Up to 12 TOP/s @ 250MHz (2 cores)</li>
<li>Provided as encrypted RTL</li>
</ul>
<h3 id="network-development-kit-ndk">Network Development Kit (NDK)</h3>
<ul>
<li>Package of code and information which is required to create ultra-small quantization DL model for Efficiera.</li>
<li>Immediate use is possible for developers working with deep learning model for GPU</li>
<li>DL framework supports PyTorch and TensorFlow 2</li>
<li>The learning environment is a GPU-equipped Linux server</li>
<li>The inference environment is a device equipped with Efficiera</li>
<li>Provide support from LeapMind</li>
</ul>
<p>LeapMind is welcoming trials and feedback from all interested parties, including system on chip (SoC) vendors and end-user product designers. To obtain Efficiera v2, please contact us at <a href="mailto:business@leapmind.io">business@leapmind.io</a>. For more product information, please visit: <a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a>.</p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate mission "To create innovative devices with machine learning and make them available everywhere." Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, many of which are centered in manufacturing, including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware</p>
<p>Head office: Shibuya Dogenzaka Sky Building 5F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL: <a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
The information in the press release is current as of the date of announcement.</p>
<p>Media Contact
Efficiera Division, Marketing and Communication Team, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>

LeapMind Announces Efficiera v2 Ultra-Low Power AI Inference Accelerator IP

<p>Tokyo Japan - LeapMind Inc., a creator of the standard in edge AI (Shibuya-ku, Tokyo; CEO: Soichi Matsuda) today announced the beta release of ultra-low power AI inference accelerator IP “Efficiera” version 2 (v2) before its commercial launch by the end of this year. Efficiera originally developed and licensed by LeapMind Inc. has been highly valued since its launch in October 2020 for its features of power saving, high performance, space saving, and performance scalability which enable easy installation to small FPGA and contribute to shortened development period of end users’ final products by supporting mass-produced boards. Upon the release of v2 beta version, LeapMind is welcoming trial use by and feedback from many people including SoC vendors and end user product designers. To obtain v2 beta version, please contact us at <a href="mailto:business@leapmind.io">business@leapmind.io</a></p>
<h2 id="main-specificationsfeatures-of-efficiera-v2">Main specifications/features of Efficiera v2</h2>
<ul>
<li>Great improvement of hardware performance: Achievement of 100TOPS/W from 27TOPS/W and speeding up of functions such as Skip connection, Convolution and Pixel Embedding</li>
<li>Flexible system development utilizing ASIC/ASSP as well as FPGA</li>
<li>Provision of extremely low bit quantization model development environment, “Efficiera NDK (Network Development Kit)” to meet the demands of end users who would like to develop individual models on Efficiera IP</li>
</ul>
<p><img src="/images/posts/en/news/7414/e62f6eaf-concept-660x371.png" alt=""></p>
<p>“It is a pleasure for us to release beta version while the development of Efficiera v2 is proceeding as scheduled”, commented Katsutoshi Yamazaki, LeapMind’s VP of Business. “Efficiera v2 is a product with a view to the use for ASIC/ASSP as well as FPGA, and its hardware performance enables image processing at the edge by utilizing machine learning. In the market, there is discussion on which is better, cloud processing or edge processing for complicated inference processing that requires large amounts of data, considering various conditions and constraints such as network load, power efficiency and real-time performance. Through our core technology, extremely low bit quantization which has developed under the assumption of inference processing using large volumes of complicated data on edge devices, we aim to help promote device development that allows users to choose inference processing on edge without hesitation. Efficiera v2 has achieved dramatically improved processing capability, especially for image processing, and our development department has confirmed that Efficiera v2 has demonstrated its great capability in situations where high-definition image processing by AI on edge devices is required. We hope that Efficiera v2 will trigger significant expansion of the conventional application scope of machine learning on edge”.</p>
<h2 id="about-efficiera">About Efficiera</h2>
<p>Efficiera is an ultra-low power AI inference accelerator IP specialized for CNN inference processing that runs as a circuit on FPGA or ASIC devices. The "ultra-small quantization" technology minimizes the number of quantization bits to 1 - 2 bits, maximizing the power and area efficiency of convolution, which accounts for most of the inference processing, without the need for advanced semiconductor manufacturing processes or special cell libraries. By using this product, deep learning functions can be incorporated into a variety of edge devices, including consumer electronics such as home appliances, industrial equipment such as construction machinery, surveillance cameras, broadcasting equipment, as well as small machines and robots that are constrained by power, cost, and heat dissipation, which has been technically difficult in the past. Visit product website at <a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a></p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate philosophy of "bringing new devices that use machine learning to the world". Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, centered in manufacturing including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware.</p>
<p>Head office: Shibuya Dogenzaka Sky Building 5F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL: <a href="https://leapmind.io/en/">https://leapmind.io/en/</a>
LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
The information in the press release is current as of the date of announcement.</p>
<p>Media Contact
Efficiera Division, Marketing and Communication Team, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>

“Beta Version of Efficiera Version 2,” Ultra-low Power AI Inference Accelerator IP

<p>June 22, 2021 - LeapMind Inc. (Shibuya-ku, Tokyo; CEO: Soichi Matsuda), a creator of the standard in edge AI today announced that it has been granted two patents for its extremely low bit quantization technology. “Two patents we acquired are one of the fundamental technologies supporting Efficiera, an ultra-low power AI inference accelerator IP that we developed and provided.” Commented Hiroyuki Tokunaga, CTO of LeapMind and one of the inventors of patents acquired. “We continue to develop related technologies and file patent applications.”</p>
<p><strong>Patent number: No. 6885645</strong>
<strong>Date of patent registration:</strong> May 17, 2021
<strong>Title of the invention:</strong> Neural network processing units, neural network processing method, and neural network processing program, neural network processing program
<strong>Patentee:</strong> LeapMind Inc.</p>
<h2 id="patent-summary">Patent Summary</h2>
<p>This patent relates to a technology that minimizes the accuracy degradation associated with low bit rates, which is an issue in extremely low bit quantization technology. This is one of the methods of quantizing multi-bit data such as images, in which the multi-bit elements in the data are replaced by quantized vectors instead of simply being reduced to lower bits. In particular, the use of this technology in the input layer, which is the first stage of a deep neural network, can be highly effective.</p>
<p><strong>Patent number: No. 6886747</strong>
<strong>Patent registration date:</strong> May 19, 2021
<strong>Title of the invention:</strong> Neural network processing units, neural network processing method, and neural network processing program, neural network processing program
<strong>Patentee:</strong> LeapMind Inc.</p>
<h2 id="patent-summary-1">Patent Summary</h2>
<p>The quantization operation is a comparison between the input value and the threshold value. Therefore, arbitrary monotonic functions between convolution and quantization operations can eventually be converted into threshold changes, which can be performed efficiently.</p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate philosophy of "bringing new devices that use machine learning to the world". Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, centered in manufacturing including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware.</p>
<p>Head office: Shibuya Dogenzaka Sky Building 5F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL：<a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>LeapMind, Efficiera and logo are trademarks or regidtered trademarks of LeapMind Inc.
The infomation in the press release is current as of the date of announcement.</p>
<p><strong>Media Contact</strong>
Marketing Communication Division, Public Relations Officer, LeapMind Inc.
Phone: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>

【Press Release】LeapMind Acquires Patents for Extremely Low Bit Quantization Technology

<p>May 20th, 2021, Tokyo Japan - LeapMind Inc., a creator of the standard in edge AI (Shibuya-ku, Tokyo; CEO: Soichi Matsuda) today announced that company’s proprietary ultra-low power AI inference accelerator IP “Efficiera” was verified RTL design for ASIC/ASSP.</p>
<p>“By conducting the design verification this time, we were able to confirm the expected PPA (Power/Performance/Area) at the time of IP configuration.” Said Katsutoshi Yamazaki, VP of Business at LeapMind. “This is a big step for us moving forward to future LSI commercialization of Efficiera”.</p>
<p>Efficiera is an ultra-low power consumption AI inference accelerator IP specialized for CNN inference arithmetic processing that operates as a circuit on FPGA devices or ASIC/ASSP devices. For more information, visit here (<a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a>).</p>
<h2 id="about-leapmind">About LeapMind</h2>
<p>LeapMind Inc. was founded in 2012 with the corporate philosophy of "bringing new devices that use machine learning to the world". Total investment in LeapMind to date has reached 4.99 billion yen (as of May 2021). The company's strength is in extremely low bit quantization for compact deep learning solutions. It has a proven track record of achievement with over 150 companies, centered in manufacturing including the automobile industry. It is also developing its Efficiera semiconductor IP, based on its experience in the development of both software and hardware.</p>
<p>Head office: Shibuya Dogenzaka Sky Building 5F, 28-1 Maruyama-cho, Shibuya-ku, Tokyo 150-0044
Representative: Soichi Matsuda, CEO
Established: December 2012
URL：<a href="https://leapmind.io/en/">https://leapmind.io/en/</a></p>
<p>*LeapMind, Efficiera and logo are trademarks or registered trademarks of LeapMind Inc.
*The information in the press release is current as of the date of announcement.</p>
<p><strong>Media Contact</strong>
Marketing Communication Division, Public Relations Officer, LeapMind Inc.
TEL: 813-6696-6267
Email: <a href="mailto:pr@leapmind.io">pr@leapmind.io</a></p>
<p><strong>Inquiries about Products and Services</strong>
Efficiera Division, Business Development Team, LeapMind Inc.
TEL: 813-6696-6267
Email: <a href="mailto:business@leapmind.io">business@leapmind.io</a>
Product official website URL: <a href="https://leapmind.io/products/efficiera-ip/">https://leapmind.io/products/efficiera-ip/</a></p>

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