This repository contains the source code for ML hardware architectures that require nearly half the number of multiplier units to achieve the same performance, by executing alternative inner-product algorithms that trade nearly half the multiplications for cheap low-bitwidth additions, while still producing identical output as the conventional inner product. This increases the theoretical throughput and compute efficiency limits of ML accelerators. See the following journal publication for the f...