A sparsity-driven tinyML accelerator for decoding hand kinematics in brain-computer interfaces

Despite advances in understanding the mechanisms of movement disorders, controlling voluntary movements remains challenging, with limited treatment options. However, the integration of machine learning (ML) accelerators into the braincomputer interface (BCI) pipeline offers promising solutions by harnessing the capabilities of ML algorithms to decode intended motor actions accurately. While there have been numerous efforts to classify intended hand movements into discrete classes, the challenging problem of decoding continuous hand kinematics has seen limited research efforts. Our work focuses on tackling this challenge using RAMAN, an energy-efficient tinyML accelerator. We demonstrate the successful decoding of macaque hand kinematics from the MC_Maze dataset on the Efinix Ti60 FPGA with RAMAN architecture. Our approach achieves an R2-score of 0.91 across 108 different maze configurations with a significantly low memory footprint of 230 KB, a latency of 86.7 ms while consuming 52.23 mW of power at a 5 MHz clock rate and 5.78 ms latency with the peak power efficiency of 102.34 GOp/s/W at 75 MHz and 75% pruning.