Welcome to EN:ai
EN:ai enables AI algorithms on different edge devices across IoT boards, FPGA boards and ASIC chips.
Powered by the low complexity and high speedup by tensorization and quantization techniques, we can provide a turnkey solution for edge devices where low-power, high-speed and accurate AI systems are required
Powerful AI with different neural network can be deployed on inexpensive IoT/ FPGA boards. And mature edge device design can further be prototyping to ASIC chip.
Edge AI runs locally and reduce the data communication in cloud based system over the air. It will greatly shorten the data accessing and processing time.
Data process and inference at local storage and not necessarily send to cloud system. No data will be collected in server.
Enable Artificial Intelligence
EN:ai, aims to utilize the emerging tensor arithmetic, added with quantization-aware design flow, for deeply compressing the computational resources needed in the NN hardware design.
The outcomes from EN:ai will address the fast-blooming needs for AI computing on the resource-limited edge and terminal devices. To enable low-power, high-speed and highly accurate board-level and chip-level implementations, innovative constrained-hardware schemes (including truncated tensor ranks and low-bitwidthquantization) will be devised to facilitate hardware-efficient NN training and inference.
Our team consists of academic and industrial experts specializing in tensor algebra and hardware implementation, as well as business operation.
Long Tao DONG
VP scientific research
Associate Professor (HKU)
VP hardware development
VP software & algorithms
VP industrial development
A-CTO (Robotics Robotics Ltd.)
Unit 936, 9/F, Building 19W, No. 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, N.T., Hong Kong