Full Stack Parallel Online Hyperdimensional Regression on FPGA

Hanning Chen, M. Hassan Najafi, Elaheh Sadredini, Mohsen Imani

Abstract: Hyperdimensional computing (HDC) has been proposed to more closely model the brain from the abstract and functionality level. Compared to the traditional sequential regression model, HDC based regression model naturally supports parallel operation, making it an ideal algorithm to be accelerated on the FPGA platform. In this paper, we propose HyDRAF, an FPGA acceleration of hyperdimensional regression supporting online learning. To overcome the computation overhead from the long-size hypervector, we introduce multiple FPGA optimizations to efficiently handle long vector access, such as on-chip storage partitioning. Furthermore, we optimize the model update process by using efficient sparse matrix representation. We also integrate the encoding module into the accelerator to realize online training by reducing off-chip DRAM access, thus enhancing FPGA resource utilization. We also evaluate the effectiveness of our approach on a wide range of regression problems. Our results show that the FPGA platform provides, on average, 11.8× speedup and 27.5× energy efficiency compared to the state-of-the-art regression method running on NVIDIA GTX 1080 GPU. On a Xilinx Alveo U200 accelerator card platform drawing less than 4 Watt for kernel Virtex Ultrascale+ XCU200 FPGA, HyDRAF demonstrates up to 1.2 million data classifications per second.

External IDs:dblp:conf/iccd/ChenNSI22