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Ultralow Power Neuromorphic Accelerator for Deep Learning Using Ni/HfO2/TiN Resistive Random Access Memory
- Source :
- 2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM).
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- In this article we explore Ni/HfO 2 /TiN resistive random access memory (RRAM) as ultralow power synaptic element in deep neural networks (DNN) for artificial intelligence applications. Low power RRAM devices are fabricated and measured, with very low RESET current and 2–3 orders of resistance window. The SET-RESET current-voltage characteristics, high- and low-resistance state statistical distribution, and analog programming characteristics are calibrated to analytical models. Training and inference for the MNIST handwritten digits dataset using a multilayer perceptron was simulated based on the calibrated model using CIMulator, a novel neuromorphic simulation platform for compute-in-memory circuitry to predict DNN inference accuracy and energy consumption. Despite larger inherent device-to-device variability due to low on current, 97% inference accuracy is achieved with only 2-bit for the weights using weight update accumulation technique.
- Subjects :
- 010302 applied physics
Computer science
business.industry
Deep learning
Inference
02 engineering and technology
Energy consumption
01 natural sciences
020202 computer hardware & architecture
Power (physics)
Resistive random-access memory
Neuromorphic engineering
Multilayer perceptron
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
Electronic engineering
Artificial intelligence
business
MNIST database
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)
- Accession number :
- edsair.doi...........aaeb2ce12b1d641e1c17f4a59c6dba19