LETTER
A high resolution and configurable 1T1R1C ReRAM macro for medical semantic segmentation
2024 Volume 21 Issue 8 Pages 20240071
Details
2024 Volume 21 Issue 8 Pages 20240071
Medical industry increasingly using convolutional neural networks (CNNs) for image processing. Nowadays, computing facilities based on Von Neumann architecture aredevoted to accelarate CNNs, yet rapidly hitting a bottlenneck in performance and energy efficiency. The computing-in-memory (CIM) architecture based on random-access memory (ReRAM) emerged as a method to overcome the issue. This work proposes a charge-domain one-transistor-one-resistor-one-capacitor (1T1R1C) CIM macro using energy-efficient charge calculation and capacitive coupling for CNNs acceleration in medical semantic segmentation. The multiplication-and-accumulation (MAC) is realized by charge distribution with a cell and capacitive coupling across different cells on a plate line. The configurable output resolution is achieved by on-chip ReRAM-based charge integral, which is energy efficient and flexible to change the output resolution. By evaluation in the 180nm technology, the proposed macro with a 64×64 array achieves a peak energy efficiency of 142.2 GOPS/W, ∼1.3X higher than previous work. The inference dice coefficient of UNet reaches 89.7%.
