Abstract:With the surging popularity of edge computing, the need to efficiently perform neural network inference on battery-constrained IoT devices has greatly increased. While algorithmic developments enable neural networks to solve increasingly more complex tasks, the deployment of these networks on edge devices can be problematic due to the stringent energy, latency, and memory requirements. One way to alleviate these requirements is by heavily quantizing the neural network, i.e. lowering the precision of the operan… Show more
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