XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks - Yin, Shihui; Jiang, Zhewei; Seo, Jae-Sun; Seok, Mingoo (IEEE journal of solid-state circuits, 2020)
We present XNOR-SRAM, a mixed-signal in-memory computing (IMC) SRAM macro that computes ternary-XNOR-and-accumulate (XAC) operations in binary/ternary deep neural networks (DNNs) without row-by-row data access. The XNOR-SRAM bitcell embeds circuits for ternary XNOR operations, which are accumulated on the read bitline (RBL) by simultaneously turning on all 256 rows, essentially forming a resistive voltage divider. The analog RBL voltage is digitized with a column-multiplexed 11-level flash analog-to-digital converter (ADC) at the XNOR-SRAM periphery. XNOR-SRAM is prototyped in a 65-nm CMOS and achieves the energy efficiency of 403 TOPS/W for ternary-XAC operations with 88.8% test accuracy for the CIFAR-10 data set at 0.6-V supply. This marks 33× better energy efficiency and 300× better energy-delay product than conventional digital hardware and also represents among the best tradeoff in energy efficiency and DNN accuracy.