Fully Row/Column-Parallel In-memory Computing SRAM Macro employing Capacitor-based Mixed-signal Computation with 5-b Inputs
Jinseok Lee, Hossein Valavi, Yinqi Tang, Naveen Verma
Abstract
This paper presents an in-memory computing (IMC) macro in 28nm for fully row/column-parallel matrix-vector multiplication (MVM), exploiting precise capacitor-based analog computation to extend from binary input-vector elements to 5-b input-vector elements, for 16x increase in energy efficiency and 5x increase in throughput. The 1152(row)x256(col.) macro employs multi-level input drivers based on a digital-switch DAC implementation, which preserve compute accuracy well beyond the 8-b resolution of the output ADCs, and whose area is halved via a dynamic-range doubling (DRD) technique. The macro achieves the highest reported IMC energy efficiency of 5796 TOPS/W and compute density of 12 TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (both normalized to 1-b ops). CIFAR-10 image classification is demonstrated with accuracy of 91%, equal to the level of ideal SW implementation.