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

TitleIVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization
Publication TypeJournal Article
Year of Publication2022
AuthorsF Liu, W Zhao, Z Wang, Y Zhao, T Yang, Y Chen, and L Jiang
JournalIeee Transactions on Computer Aided Design of Integrated Circuits and Systems
Date Published01/2022

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 applys 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. 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 intra-crossbar data path because the weight is stationary on the crossbar and bind to the “add” operation along the bit-line. Therefore, many non-uniform quantization methods must rollback the quantization before mapping the weights onto the crossbar. Counter-intuitively, this paper 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 same magnitude along the same bit-line 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 inter-crossbar data path. To further squeeze the weights on fewer crossbars, we decouple the intra-crossbar 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 inter-crossbar data paths. Putting all together, IVQ achieves 19.7×, 10.7×, 4.7×∼63.4×, 91.7× speedup, and 17.7×, 5.1×, 5.7×∼68.1×, 541× energy savings over two PIM accelerators (ISAAC and CASCADE), two customized quantization accelerators (based on ASIC and FPGA), and NVIDIA RTX 2080 GPU, respectively.

Short TitleIeee Transactions on Computer Aided Design of Integrated Circuits and Systems