On training networks of monostable multivibrator timer neurons

An important bottleneck in present-day neuromorphic hardware is its reliance on synaptic addition, which limits the achievable degree of parallelization and thus processing throughput. We present a network of monostable multivibrator timers, whose synaptic inputs are simply OR-ed together, thus mitigating the synaptic addition bottleneck. Monostable multivibrators are simple timers which are easily implemented using counters in digital hardware and can be interpreted as non biologically-inspired spiking neurons. We show how fully binarized event-driven recurrent networks of monostable multivibrators can be trained to solve classification tasks. Our training algorithm resolves temporally overlapping input events. We demonstrate our approach on the MNIST handwritten digits, Google Soli radar gestures, IBM DVS128 gestures and Yin-Yang classification tasks. The estimated energy consumption for the MNIST handwritten digits task, excluding the final linear readout layer, is 855pJ per inference for a test accuracy of $98.61\%$ for a reconfigurable network of 500 units, when mapped to the TSMC HPC+ $28\mathrm{nm}$ process.