Data accesses between on- and off-chip memories account for a large fraction of overall energy consumption during inference with deep learning networks. On-chip memory compression can greatly reduce this energy cost as long as it balances the simplicity and low cost of the compression/decompression implementation and with its \emph{effectiveness} in data size reduction. We present Boveda, a simple and effective on-chip lossless memory compression technique for fixed-point precision networks. It…