Announces Marketing Partnership with CBC Co in Japan
AlphaICs, a leading AI fabless semiconductor company that develops edge inference and edge learning technologies, has announced the availability of engineering samples of ‘Gluon’ – an 8 TOPS Edge AI inference co-processor to customers in surveillance, industrial, retail, auto, and Industrial IoT verticals which carries best-in-class FPS/Watt performance.
Gluon will be shipped with a complete (Software Development Kit) SDK that enables easy deployment of neural networks. The advanced edge inference chip delivers the capability for customers to add AI capability in the current X86 / ARM-based systems, resulting in significant cost savings.
Gluon provides the best fps/watt performance in the market for classification and detection Neural Networks – 32 Frames Per Second (FPS)/watt for Yolo-V2, a leading object detection model & 22 Frames Per Second (FPS)/Watt for VGG-19, a leading classification model.
Gluon is currently being sampled to for early customers to enable the development of their vision applications. It is engineered for OEMs and solution providers targeting vision market segments, such as surveillance, industrial, retail, Industrial IoT, and edge gateway manufacturers.
To accelerate its market foray into highly demanding silicon markets, AlphaICs has established a channel partner relationship with CBC Co. Ltd, a Japanese enterprise offering video surveillance products for their customers.
‘CBC has been working with AlphaICs for close to two years and we are excited to be its marketing partner in Japan. Gluon was showcased at Japan AI Expo in October 2021 and generated great interest from Japanese customers for vision applications based on its superior performance. AlphaICs co-processor strategy is well received, and we are very excited to take this technology to our customers” said Kazuhiko Kondo, Executive Officer, CBC Co., Ltd.
AlphaICs CEO Pradeep Vajram said “We are pleased with our Gluon silicon results and are now demonstrating the innovative technology to our customers. Our team worked very hard to design this high-performance, industry resonating deep-learning co-processor. Gluon is future-ready and is well-positioned to address the AI vision applications for surveillance, retail, industrial, and smart city markets.”
Early last year the company raised $8 million to advance the development of Gluon based on the proprietary architecture RAPTM. AlphaICs’ highly scalable and modular architecture uses a specialized Instruction Set Architecture that is specifically optimized for AI.
How different is Gluon from the current generation of edge inference Chip?
Gluon, an 8 TOPS Edge AI co-processor, is produced using the 16nm FinFET process at TSMC. This chip accelerates Deep Learning Neural Network models for classification, detection, and segmentation and is focused on vision applications. Gluon incorporates PCIe and LPDDR4 interfaces to enable high-speed transfers to host processors and DRAM, respectively.
Based on many innovations, Gluon provides best-in-class benchmarks:
- 153 frames per second with Yolo-V2, a leading object detection model (416x416x3 image size) in 4.73 watts.
- 79 frames per second with VGG-19, a leading classification model (224x224x3) in 3.6 watts.
AlphaICs is a leading AI technology company that develops edge inference and edge learning technologies to enable AI at the edge. AlphaICs has developed a next-generation AI architecture, called Real AI Processor (RAPTM). Architecture provides high performance, low power, and minimal latency, enabling best-in-class edge AI inference processors. RAPTM architecture also supports edge learning to reduce training data requirements, enables auto labeling and continuous learning at the edge. The company is led by a team of technology experts and successful serial entrepreneurs committed to putting forth the true potential of AI at the edge. The company has operations in Milpitas, US, and Bangalore, India. AlphaICs is currently a company in the Silicon Catalyst Incubator. Learn more at https://www.alphaics.ai