A FPGA Friendly Approximate Computing Framework with Hybrid Neural Networks: (Abstract Only)

Neural approximate computing is promising to gain energy-efficiency at the cost of tolerable quality loss. The architecture contains two neural networks: the approximate accelerator generates approximate results while the classifier determines whether input data can be safely approximated. However, they are not compatible to a heterogeneous computing platform, due to the large communication overhead between the approximate accelerator and accurate cores, and the large speed gap between them. This paper proposes a software-hardware co-design strategy. With deep exploration of data distributions in the feature space, we first propose a novel approximate computing architecture containing a multi-class classifier and multiple approximate accelerator; this architecture, derived by the existing iterative co-training methods, can shift more data from accurate computation (in CPU) to approximate accelerator (in FPGA); the increased invocation of the approximate accelerator thus can yield higher utilization of the FPGA-based accelerator, resulting in the enhanced the performance. Moreover, much less input data is redistributed, by the classifier (also in FPGA), back to CPU, which can minimize the CPU-FPGA communication. Second, we design a pipelined data-path with batched input/output for the proposed hybrid architecture to efficiently hide the communication latency. A mask technique is proposed to decouple the synchronization between CPU and FPGA, in order to minimize the frequency of communication.