TF2, FPGA-based AI computing framework, introduced by InspurNews
September 17, 2019
SAN JOSE, Calif. Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first deep neural network (DNN) shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models.
This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA.
TF2 is able to implement FPGA inference based on mainstream AI training software and the DNN model, intended to enable users to maximize FPGA computing power and achieve the high-performance and low-latency deployment of FPGAs. According to the company, chip-level AI design and performance verification can also be carried out quickly with the TF2 computing architecture.
TF2 consists of two parts: the first being the model optimization and conversion tool TF2 Transform Kit, which can conduct compression, pruning, and 8-bit quantization of network model data trained by frameworks. The second part is the FPGA intelligent running engine TF2 Runtime Engine, which can automatically convert optimized model files into FPGA target running files.