  Implantable Brain-machine interfaces (BMIs) are promising for motor
rehabilitation and mobility augmentation, and they demand accurate and
energy-efficient algorithms. In this paper, we propose a novel spiking neural
network (SNN) decoder for regression tasks for implantable BMIs. The SNN is
trained with enhanced spatio-temporal backpropagation to fully leverage its
capability to handle temporal problems. The proposed SNN decoder outperforms
the state-of-the-art Kalman filter and artificial neural network (ANN) decoders
in offline finger velocity decoding tasks. The decoder is deployed on a
RISC-V-based hardware platform and optimized to exploit sparsity. The proposed
implementation has an average power consumption of 0.50 mW in a duty-cycled
mode. When conducting continuous inference without duty-cycling, it achieves an
energy efficiency of 1.88 uJ per inference, which is 5.5X less than the
baseline ANN. Additionally, the average decoding latency is 0.12 ms for each
inference, which is 5.7X faster than the ANN implementation.
