One-Time Programmable Memory for Ultra-Low Power ANN Inference Accelerator With Security Against Thermal Fault Injection - Shreyas Deshmukh; Ankit Bende; Diti Sanghai; Vivek Saraswat; Anmol Biswas; Abhishek Kadam; Shubham Patil; Ajay Kumar Singh; Veeresh Deshpande; Udayan Ganguly (IEEE Journal of the Electron Devices Society)
Memory technologies for in-memory computing (IMC) based neural network (NN) operations in edge devices face two primary challenges. First, these technologies often exhibit an excessive average power due to the high currents in the memory’s low and high resistance state (LRS and HRS). Second, their vulnerability to tampering compromises device reliability in security-sensitive Internet of Things (IoT) applications. To tackle these challenges, we propose the use of one-time programmable memory (OTPM) for IMC-based thermal attack-secured and ultra-low powered NN accelerator with write disablement to prevent re-programming/tampering. In this article, first, we show that HRS for OTP memory is exceptionally high (~190 M $Ω $ ), but the higher current in LRS (~100 mA) presents a challenge of high average power. We address this challenge by introducing a select transistor gate voltage biasing scheme with the 1T-1OTPM bit cell to reduce LRS current significantly while still trading off the excessive HRS/LRS ratio of 1.5 M to about 1 k. This reduces average power by $\sim 1000× $ without degrading CIFAR-10 classification performance. Second, we evaluate the impact of OTPM size scaling on HRS and LRS distributions. Though scaling reduces HRS current, we show that moderately scaled 10F $× 10$ F OTPM bit cells produce the lowest maximum current in HRS compared to the more aggressively scaled 1F $× 1$ F, which suffers from significant process-induced variability. Third, we show the robustness to temperature, which enables security from thermal attacks.