NEUCHIPS' Purpose-Built Accelerator Designed to Be Industry's Most Efficient Recommendation Inference Engine
Team comes out of Stealth to Introduce Innovative AI Inference Platform
LOS ALTOS, Calif., June 2, 2022 - NEUCHIPS is excited to announce its first ASIC, RecAccelTM N3000 using TSMC 7nm process, and specifically designed for accelerating deep learning recommendation models (DLRM). NEUCHIPS has partnered with industry leaders in Taiwan's semiconductor and cloud server ecosystem and plans to deliver its RecAccel™ N3000 AI inference platform on Dual M.2 modules for Open Compute Platform compliant servers as well as PCIe Gen 5 cards for standard data center servers during the 2H'2022.
"In 2019, when Facebook open sourced their Deep Learning Recommendation Model and challenged the industry to deliver a balanced AI inference chip platform, we decided to pursue the challenge," said Dr. Lin, NEUCHIPS CEO, Co-Founder of Global Unichip Corp, subsidiary of TSMC and Professor at National Tsing Hua University, Taiwan. "Our continued improvements in MLPerf DLRM benchmarking and whole-chip emulation give us confidence that our RecAccel™ AI hardware architecture co-designed with our software will scale to deliver industry leadership and exceed our target of 20M inferences per second at 20 Watts."
NEUCHIPS RecAccel™ N3000 Inference platform includes sophisticated hardwired accelerators, patented query scheduling and a comprehensive software stack optimized to provide high accuracy and hardware utilization while maintaining energy efficiency required in data centers. Other key features include the following:
- Proprietary 8-bit coefficient quantization, calibration and hardware support that deliver 99.95% of FP32 accuracy.
- Patented embedding engine with novel cache design and DRAM traffic optimization that reduces LPDDR5 access by 50% and increases bandwidth utilization by 30%.
- Dedicated MLP compute engines that deliver state-of-the-art energy efficiency at engine level, and 1 microjoule per inference at SOC level.
- Proven software stack that delivers very high scalability across multiple cards.
- Support for leading recommender AI models including DLRM, WND, DCN, and NCF.
- Robust security based on hardware root of trust.
About Neuchips:
NEUCHIPS develops purpose-built AI inference chip platforms from the ground up, by co-developing hardware and software to meet our customer's requirements for performance, accuracy, power, and cost-efficiency. NEUCHIPS is a founding member of MLCommons™. For more information, please visit https://www.neuchips.ai or contact contact@neuchips.ai
|
Neuchips Hot IP
Related News
- NEUCHIPS Secures $20 Million in Series B2 Funding to Deliver AI Inference Platform for Deep Learning Recommendation
- Xilinx Launches Alveo U55C, Its Most Powerful Accelerator Card Ever, Purpose-Built for HPC and Big Data Workloads
- Flex Logix Announces Working Silicon Of Fastest And Most Efficient AI Edge Inference Chip
- NEUCHIPS Announces World's First Deep Learning Recommendation Model (DLRM) Accelerator: RecAccel
- P.A. Semi Successfully Develops the Most Power-Efficient High-Performance Processor Ever Designed
Breaking News
- Cadence to Acquire Secure-IC, a Leader in Embedded Security IP
- Blue Cheetah Tapes Out Its High-Performance Chiplet Interconnect IP on Samsung Foundry SF4X
- Alphawave Semi to Lead Chiplet Innovation, Showcase Advanced Technologies at Chiplet Summit
- YorChip announces patent-pending Universal PHY for Open Chiplets
- PQShield announces participation in NEDO program to implement post-quantum cryptography across Japan
Most Popular
- Alphawave Semi to Lead Chiplet Innovation, Showcase Advanced Technologies at Chiplet Summit
- Altera Launches New Partner Program to Accelerate FPGA Solutions Development
- Electronic System Design Industry Posts $5.1 Billion in Revenue in Q3 2024, ESD Alliance Reports
- Breaking Ground in Post-Quantum Cryptography Real World Implementation Security Research
- YorChip announces patent-pending Universal PHY for Open Chiplets
E-mail This Article | Printer-Friendly Page |