IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization

Weight quantization is well adapted to cope with the ever-growing complexity of the deep neural network (DNN) model. Diversified quantization schemes lead to diverse quantized bit width and formats of the weights, thereby, subject to different hardware implementations. Such variety prevents a general NPU to leverage different quantization schemes to gain performance and energy efficiency. More importantly, a trend of quantization diversity emerges that applies multiple quantization schemes to different fine-grained structures (e.g., a layer or a channel of weight) of a DNN. Therefore, a general architecture is desired to exploit varied quantization schemes. The crossbar-based processing-in-memory (PIM) architecture, a promising DNN accelerator, is well known for its highly efficient matrix-vector multiplication. However, PIM suffers from the inflexible intracrossbar data path because the weight is stationary on the crossbar and binds to the “add” operation along the bitline. Therefore, many nonuniform quantization methods must rollback the quantization before mapping the weights onto the crossbar. Counterintuitively, this article discovers a unique opportunity of the PIM architecture to exploit varied quantization schemes. We first transform the quantization diversity problem into a consistency problem by aligning the bit with the same magnitude along the same bitline of the crossbar. Consequently, such naive weight mapping causes many square hollows of idle PIM cells. We then propose a novel spatial mapping to exempt these “hollow” crossbar from the intercrossbar data path. To further squeeze the weights on fewer crossbars, we decouple the intracrossbar data path from the hardware bitline by a novel temporal scheduling, so that bits with different magnitudes can be placed on cells along the same bitline. Finally, the proposed IVQ includes a temporal pipeline to avoid the introduced stalling cycles, and a data flow with delicate control mechanisms for the new intra and intercrossbar data paths. Putting all together, IVQ achieves <inline-formula> <tex-math notation="LaTeX">$19.7\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$10.7\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$4.7\times \sim 63.4\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$91.7\times $ </tex-math></inline-formula> speedup, and <inline-formula> <tex-math notation="LaTeX">$17.7\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5.1\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5.7\times \sim 68.1\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$541\times $ </tex-math></inline-formula> energy savings over two PIM accelerators (ISAAC and CASCADE), two customized quantization accelerators (based on ASIC and FPGA), and NVIDIA RTX 2080 GPU, respectively.