  IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and
achieves ultrahigh execution parallelism and power efficiency. However, in
TrueNorth chip, low quantization resolution of the synaptic weights and spikes
significantly limits the inference (e.g., classification) accuracy of the
deployed neural network model. Existing workaround, i.e., averaging the results
over multiple copies instantiated in spatial and temporal domains, rapidly
exhausts the hardware resources and slows down the computation. In this work,
we propose a novel learning method on TrueNorth platform that constrains the
random variance of each computation copy and reduces the number of needed
copies. Compared to the existing learning method, our method can achieve up to
68.8% reduction of the required neuro-synaptic cores or 6.5X speedup, with even
slightly improved inference accuracy.
