NN-AdderNet: Nonnegative and Sparse Weight Optimization Towards Ultra-Low Bitwidth AdderNet Quantization and Compression

Emerging efficient deep neural network (DNN) models, such as AdderNet, have shown great promise in significantly improving hardware efficiency compared to traditional convolutional neural networks (CNNs). However, achieving low bitwidth quantization and effective model compression remains a major challenge. To this end, we introduce Nonnegative AdderNet (NNAdderNet), a quantization- and compression-friendly AdderNet variant that enables model compression down to 4 bits or even lower. We begin by proposing an equivalent transformation of the sum-of-absolute-difference (SAD) kernel in AdderNet, which allows for the formulation of nonnegative weights. This transformation effectively eliminates the need for a sign bit, thus saving 1 bit per weight. Next, we propose to exploit the dual-sparsity pattern in the weights of the activation-oriented NN-AdderNet quantized model. This inherent sparsity enhances the lossless compression performance over NN-AdderNet. Experimental results show that NN-AdderNet can achieve an average compressed weight bitwidth down to 4 bits or even lower, while achieving negligible accuracy loss as compared to full-precision AdderNet models. Such benefits are further illustrated with hardware-level energy and latency improvements in designing DNN inference accelerators. Consequently, the NN-AdderNet model exhibits both algorithmic and hardware efficiency, thus making it a promising candidate for resource-limited applications.