iMAD: An In-Memory Accelerator for AdderNet with Efficient 8-bit Addition and Subtraction Operations

Adder Neural Network (AdderNet) is a new type of Convolutional Neural Networks (CNNs) that replaces the computational-intensive multiplications in convolution layers with lightweight additions and subtractions. As a result, AdderNet preserves high accuracy with adder convolution kernels and achieves high speed and power efficiency. In-Memory Computing (IMC) is known as the next-generation artificial-intelligence computing paradigm that has been widely adopted for accelerating binary and ternary CNNs. As AdderNet has much higher accuracy than binary and ternary CNNs, accelerating AdderNet using IMC can obtain both performance and accuracy benefits. However, existing IMC devices have no dedicated subtraction function, and adding subtraction logic may bring larger area, higher power, and degraded addition performance. In this paper, we propose iMAD as an in-memory accelerator for AdderNet with efficient addition and subtraction operations. First, we propose an efficient in-memory subtraction operator at the circuit level and co-optimize the addition performance to reduce the latency and power. Second, we propose an accelerator architecture for AdderNet with high parallelism based on the optimized operators. Third, we propose an IMC-friendly computation pipeline for AdderNet convolution at the algorithm level to further boost the performance. Evaluation results show that our accelerator iMAD achieves 3.25X speedup and 3.55X energy efficiency compared with a state-of-the-art in-memory accelerator.