Edge Impulse and BrainChip Partner to Further AI Development with Support for the Akida platform
SAN JOSE, Calif., Jan. 3, 2023 -- Edge Impulse announced today official support for BrainChip's neural processor AI IP, making BrainChip the first strategic IP partner on the Edge Impulse platform. This integration will enable users to leverage the power of Edge Impulse's machine learning platform, combined with the high-performance neural processing capabilities of BrainChip's Akida™ to develop and deploy powerful edge-based solutions.
Among the products supported by Edge Impulse is BrainChip's AKD1000 SoC, the first available device with Akida IP for developers. This comes with a BrainChip Akida PCIe reference board, which can be plugged into a developer's existing system to unlock capabilities for a wide array of edge AI use cases, including Automotive, Consumer, Home, and Industrial applications.
The Akida IP platform provides low-power, high-performance edge AI acceleration, designed to enable real-time machine learning inferencing on-device. Based on neuromorphic principles that mimic the brain, Akida supports today's models and workloads while future-proofing for emerging trends in efficient AI. Now devices with the Akida IP supported by Edge Impulse can enable users to sample raw data, build models, and deploy trained embedded machine learning models directly from Edge Impulse Studio to create the next generation of low-power, high-performance ML applications.
"This integration will provide users with a powerful and easy-to-use solution for building and deploying machine learning models on the edge," said Zach Shelby, co-founder and CEO of Edge Impulse. "We look forward to seeing what our users will create with BrainChip's AI offering."
"BrainChip's goal is to push the limits of on-chip AI compute to extremely energy-constrained sensor devices, the kind of performance that is only available in much higher power systems." said Sean Hehir, BrainChip's CEO. "Having our Akida IP supported and implemented into the Edge Impulse platform helps ensure that developers are able to deploy ML solutions quickly and easily to create a much more capable, innovative, and truly intelligent edge."
Edge Impulse and BrainChip have an established relationship, previously announcing cross-platform support, including support for deploying Edge Impulse projects on the BrainChip MetaTF platform. Some of the features in which the user community can leverage include:
- BrainChip's transfer learning block on Edge Impulse design studio
- Quantization Aware Training (QAT)
- The introduction of FOMO for BrainChip's Akida
- Generation of BrainChip's compatible Edge Learning Models
- No-code binary generation for quick AKD1000 deployment
- Performance metrics for model profiling
The ongoing combination of BrainChip's Akida technology and Edge Impulse's platform, tools, and services will allow customers to achieve their ML objectives with fast and efficient development cycles to get to market quicker and achieve a competitive advantage.
Visit the documentation page to learn more about how the collaboration between Edge Impulse and BrainChip can benefit edge ML projects.
About Edge Impulse
Edge Impulse is the leading machine learning platform, enabling all enterprises to build smarter edge products. Their technology empowers developers to bring more ML products to market faster, and helps enterprise teams rapidly develop industry-specific solutions in weeks instead of years. The Edge Impulse platform provides powerful automation and low-code capabilities to make it easier to build valuable datasets and develop advanced ML with streaming data. With over 58,000 developers, and partnerships with the top silicon vendors, Edge Impulse offers a seamless integration experience to validate and deploy with confidence across the largest hardware ecosystem. To learn more, visit edgeimpulse.com.
About BrainChip Holdings Ltd
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company's first-to-market neuromorphic processor, Akida™, mimics the human brain to analyze only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Keeping machine learning local to the chip, independent of the cloud, also dramatically reduces latency while improving privacy and data security. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers' products, as well as the planet. Explore the benefits of Essential AI at brainchip.com.
|
Related News
- Ceva and Edge Impulse Join Forces to Enable Faster, Easier Development of Edge AI Applications
- BrainChip Makes Second-Generation Akida Platform Available to Advance State of Edge AI Solutions
- Edge Impulse Releases Deployment Support for BrainChip Akida Neuromorphic IP
- BrainChip Unveils Edge AI Box Partner Ecosystem for Gestures, Cybersecurity, Image Recognition, and Computer Vision
- BrainChip Begins Accepting Pre-Orders of the Akida Edge AI Box
Breaking News
- Breker RISC-V SystemVIP Deployed across 15 Commercial RISC-V Projects for Advanced Core and SoC Verification
- Veriest Solutions Strengthens North American Presence at DVCon US 2025
- Intel in advanced talks to sell Altera to Silverlake
- Logic Fruit Technologies to Showcase Innovations at Embedded World Europe 2025
- S2C Teams Up with Arm, Xylon, and ZC Technology to Drive Software-Defined Vehicle Evolution
Most Popular
- Intel in advanced talks to sell Altera to Silverlake
- Arteris Revolutionizes Semiconductor Design with FlexGen - Smart Network-on-Chip IP Delivering Unprecedented Productivity Improvements and Quality of Results
- RaiderChip NPU for LLM at the Edge supports DeepSeek-R1 reasoning models
- YorChip announces Low latency 100G ULTRA Ethernet ready MAC/PCS IP for Edge AI
- AccelerComm® announces 5G NR NTN Physical Layer Solution that delivers over 6Gbps, 128 beams and 4,096 user connections per chipset
![]() |
E-mail This Article | ![]() |
![]() |
Printer-Friendly Page |