BrainChip Joins Intel Foundry Services to Advance Neuromorphic AI at the Edge
Laguna Hills, Calif. – December 11, 2022 –BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power neuromorphic AI IP, today announced that it has become a member of the Intel Foundry Services (IFS) ecosystem alliance to help advance innovation on Intel’s foundry manufacturing platform.
BrainChip is the latest industry-leading IP partner to join the IFS Accelerator – IP Alliance. Partners in this alliance collaborate with IFS to enable designers to access high-quality IPs, supporting their design needs and project schedule, while optimizing for performance, power and area. Building upon Intel’s advanced technology, the IP portfolios of IFS Accelerator include all the essential IP blocks needed for modern Systems-On-Chip (SoC), such as standard cell libraries, embedded memories, general purpose I/Os, analog IP and interface IP.
Related |
Edge AI Accelerator NNE 1.0 ![]() |
A new generation of devices that demand independent learning and inference capabilities, faster response times and limited power consumption has created opportunities for new products with smarter sensors, devices, and systems. Integrating AI into the SoC delivers efficient compute and the unique learning and performance requirements of Edge AI. BrainChip’s AkidaTM, enables low-latency and ultra-low power AI inference and on-chip learning.
“We are proud to partner with Intel as part of its IFS Accelerator – IP Alliance,” said Anil Mankar, Chief Development Officer at BrainChip. “The combination of BrainChip’s Akida IP and Intel’s leading technology helps ensure that customers looking to implement edge AI acceleration and learning have the tools and resources to accelerate their success.”
Additional details about the BrainChip and Intel Foundry Services Ecosystem Alliance is available at BrainChip.com
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market neuromorphic processor, AkidaTM, 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 www.brainchip.com.
|
Related News
- BrainChip and CVEDIA Team to Advance State-of-the-Art Edge AI and Neuromorphic Computing
- Cadence Joins Intel Foundry Services Ecosystem Alliance to Advance Chip Design Innovation
- Faraday Joins Intel Foundry Accelerator Design Services Alliance to Target Advanced Applications
- Flex Logix Joins Intel Foundry Services Accelerator IP Alliance to Enable Fast, Low Power, Reconfigurable SoC's
- BrainChip Previews Industry's First Edge Box Powered by Neuromorphic AI IP
Breaking News
- Ceva-Waves Wi-Fi 6 IP Powers WUQI Microelectronics Wi-Fi/Bluetooth Combo Chip
- Accellera Board Approves Universal Verification Methodology for Mixed-Signal (UVM-MS) 1.0 Standard for Release
- Mirabilis Design Adds System-Level Modelling Support for Industry-Standard Arteris FlexNoC and Ncore Network-on-Chip IPs
- Rambus Reports Fourth Quarter and Fiscal Year 2024 Financial Results
- CoMira Solutions unveils its new 1.6T Ethernet UMAC IP
Most Popular
- Intel Halts Products, Slows Roadmap in Years-Long Turnaround
- UK Space Agency Awards EnSilica £10.38m for Satellite Broadband Terminal Chips
- CoMira Solutions unveils its new 1.6T Ethernet UMAC IP
- Eighteen New Semiconductor Fabs to Start Construction in 2025, SEMI Reports
- RISC-V in Space Workshop 2025 in Gothenburg
![]() |
E-mail This Article | ![]() |
![]() |
Printer-Friendly Page |