Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

二维铁电半导体层级处理模块设计及低功耗高性能人工视觉系统应用

The growth of data and Internet of Things challenges traditional hardware,which encounters efficiency and power issues owing to separate functional units for sensors,memory,and computation.In this study,we designed an a-phase indium selenide(a-In_(2)Se_(3))transistor,which is a two-dimensional ferroelectric semiconductor as the channel material,to create artificial optic-neural and electro-neural synapses,enabling cutting-edge processing-in-sensor(PIS)and computing-in-memory(CIM)functionalities.As an optic-neural synapse for low-level sensory processing,the a-In_(2)Se_(3)transistor exhibits a high photoresponsivity(2855 A/W)and detectivity(2.91×10^(14)Jones),facilitating efficient feature extraction.For high-level processing tasks as an electro-neural synapse,it offers a fast program/erase speed of 40 ns/50μs and ultralow energy consumption of 0.37 aJ/spike.An AI vision system using a-In_(2)Se_(3)transistors has been demonstrated.It achieved an impressive recognition accuracy of 92.63%within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities.This study demonstrates the potential of the a-In_(2)Se_(3)transistor in future vision hardware,enhancing processing,power efficiency,and AI applications.