power consumption

main memory

edge devices

networks cnns

IEEE Journal of Solid-state Circuits

This article presents a programmable in-memory computing accelerator (PIMCA) for low-precision (1–2 b) deep neural network (DNN) inference. The custom 10T1C bitcell in the in-memory computing (IMC) macro has four additional transistors and one capacitor to perform capacitive-coupling-based multiply and accumulation (MAC) in analog-mixed-signal (AMS) domain. A macro containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256 \times 128$ </tex-math></inline-formula> bitcells can simultaneously activate all the rows, and as a result, it can perform a matrix-vector multiplication (VMM) in one cycle. PIMCA integrates 108 of such IMC static random-access memory (SRAM) macros with the custom six-stage pipeline and the custom instruction set architecture (ISA) for instruction-level programmability. The results of IMC macros are fed to a single-instruction-multiple-data (SIMD) processor for other computations such as partial sum accumulation, max-pooling, activation functions, etc. To effectively use the IMC and SIMD datapath, we customize the ISA especially by adding hardware loop support, which reduces the program size by up to 73%. The accelerator is prototyped in a 28-nm technology, and integrates a total of 3.4-Mb IMC SRAM and 1.5-Mb off-the-shelf activation SRAM, demonstrating one of the largest IMC accelerators to date. It achieves the system-level energy efficiency of 437 TOPS/W and the peak throughput of 49 TOPS at the 42-MHz clock frequency and 1-V supply for the VGG9 and the ResNet-18 on the CIFAR-10 dataset.

PIMCA: A Programmable In-Memory Computing Accelerator for Energy-Efficient DNN Inference