LAUSR

Machine Learning (ML) workloads being memory- and compute-intensive, consume large amounts of power running on conventional computing systems, restricting their implementations to large-scale data centers. Transferring large amounts of data from the edge devices to the data centers is not only energy expensive, but sometimes undesirable in security-critical applications. Thus, there is a need for building domain-specific hardware primitives for energy-efficient ML processing at the edge. One such approach - in-memory computing, eliminates frequent and unnecessary data-transfers between the memory and the compute units, by directly computing the data where it is stored. However, the analog nature of computations introduces non-idealities, which degrades the overall accuracy of neural networks. In this paper, we propose an in-memory computing primitive for accelerating dot-products within standard 8T-SRAM caches, using charge-sharing. The inherent parasitic capacitance of the bitlines and sourcelines is used for accumulating analog voltages, which can be sensed for an approximate dot product. The charge sharing approach involves a self-compensation technique which reduces the effects of non-idealities, thereby reducing the errors. Our results for ternary weight neural networks show that using the proposed compensation approaches, the accuracy degradation is within 1% and 5% of the baseline accuracy, for the MNIST and CIFAR-10 dataset, respectively, with an energy-delay product improvement of $38\times $ over the standard von-Neumann computing system. We believe that this work can be used in conjunction with existing mitigation techniques, such as re-training approaches, to further enhance system performance.