TRIM: Thermal Auto-Compensation for Resistive In-Memory Computing

In-Memory Computing (IMC) has emerged as one of the most promising architectures to efficiently compute artificial intelligence tasks on hardware, particularly Deep Neural Networks (DNNs). IMC can make use of analog computation principles alongside emerging Non-Volatile Memory (eNVM) technologies, potentially offering several orders of magnitude increased energy efficiency compared to generic processing units. Yet, the use of analog circuitry, potentially integrated with emerging technologies post-processed on top of silicon wafers, increases the susceptibility of hardware to a large spectrum of variations, for instance manufacturing, noise or temperature sensitivity. Hence, this susceptibility can hamper the large-scale deployment of IMC circuits into the market. To tackle the reliability of analog resistive-based IMC circuits regarding temperature variations, this paper presents TRIM, a thermal on-chip auto-compensation method aimed at fully calibrating first-order temperature effects. TRIM is designed to maintain the computational accuracy of IMC cores in DNN applications over a wide temperature range, while being highly scalable and adaptable. In essence, the temperature compensation is realized through a Complementary-To-Absolute-Temperature (CTAT) voltage reference integrated inside a voltage regulator and applied at the zero reference node of a Multiplying Digital-to-Analog Converter (MDAC), eliminating the need for external circuits or look-up tables. The proposed methodology is demonstrated on a proof-of-concept 65 nm CMOS resistive IMC column. Measurement results showcase that the proof-of-concept auto-compensation system significantly enhances inference and Multiply-And-Accumulate (MAC) operation accuracy of any first-order resistive crossbar column, achieving inference accuracy recovery of 100% over a temperature range of -20 °C to 60 °C and a 91.3% improvement in MAC operation accuracy, with an area overhead of 2% and power overhead of <0.02%.