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A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas
With an energy per classification of 313nJ at 0. 6V and a 0. 32-mm$^2$ area for accuracies of 95. 3% (on-chip training) and 97. 5% (off-chip training) on MNIST, we demonstrate that SPOON reaches the efficiency of conventional machine learning accelerators while embedding on-chip learning and being compatible with event-based sensors, a point that we further emphasize with N-MNIST benchmarking.
MorphIC: A 65-nm 738k-Synapse/mm$^2$ Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices.
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