Time Domain Near Memory Computing Engine

Abstract

The increasing computational demand of AI workloads has intensified the need for energy-efficient in-memory and near-memory computing architectures, particularly because data movement often consumes significantly more energy than computation itself. While fully digital architectures provide robust scalability and support higher-resolution computation, analog in-memory computing has demonstrated improved energy efficiency for low-precision workloads. However, its reliance on peripheral DACs and ADCs introduces additional power, area, and design overhead. To address these challenges, this work presents a time-domain near-memory computing architecture for low-precision multiply-and-accumulate (MAC) operations. In the proposed approach, digital weight bits stored in SRAM are converted using a current-steering DAC, while the digital input vector is encoded by an N-pulse generator. This enables multiplication to be performed in the time domain while maintaining a digital-friendly interface. Two accumulation schemes, a delay-cell-based architecture and a counter-based architecture, are investigated and compared in terms of design trade-offs, linearity, scalability, and power efficiency. To improve technology portability, the N-pulse generator and counters are implemented using RTL synthesis, while the current-steering DAC remains in the analog domain. A 4 x 4 MAC prototype is implemented with a 1 V supply, achieving an operating frequency of 40 MHz, power consumption of 42 uW, and energy efficiency of 7.62 TOPS/W.