WAGONN: Weight Bit Agglomeration in Crossbar Arrays for Reduced Impact of Interconnect Resistance on DNN Inference Accuracy

Deep neural network (DNN) accelerators employing crossbar arrays capable of in-memory computing (IMC) are highly promising for neural computing platforms. However, in deeply scaled technologies, interconnect resistance severely impairs IMC robustness, leading to a drop in the system accuracy. To address this problem, we propose WAGONN - a technique based on agglomerating weight bits in crossbar arrays which alleviates the detrimental effect of wire resistance. For 8T-SRAM-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula> crossbar arrays in 7nm technology, WAGONN enhances the accuracy from 47.78% to 83.5% for ResNet-20/CIFAR-10. We also show that WAGONN can be used synergistically with Partial-Word-Line-Activation, further boosting the accuracy. Further, we evaluate the implications of WAGONN for compact ferroelectric transistor-based crossbar arrays and show accuracy enhancement. WAGONN incurs minimal hardware overhead, with less than a 1% increase in energy consumption. Additionally, the latency and area overheads of WAGONN are ~1% and ~16%, respectively when 1 ADC is utilized per crossbar array.