An Energy-Efficient One-Shot Time-Based Neural Network Accelerator Employing Dynamic Threshold Error Correction in 65 nm

摘要

As neural networks continue to infiltrate diverse application domains, computing will begin to move out of the cloud and onto edge devices necessitating fast, reliable, and low-power (LP) solutions. To meet these requirements, we propose a time-domain core using one-shot delay measurements and a lightweight post-processing technique, dynamic threshold error correction (DTEC). This design differs from traditional digital implementations in that it uses the delay accumulated through a simple inverter chain distributed through an SRAM array to intrinsically compute resource intensive multiply-accumulate (MAC) operations. Implemented in 65-nm LP CMOS, we achieve an energy efficiency of 104.8 TOp/s/W at 0.7-V with 3b resolution for 19.1 fJ/MAC.