For this case study, we have developed a quantized large-scale ResNet-50 network using ImageNet [265] benchmark with high accuracy. We further show that the quantized ResNet-50 network can be realized on ReRAM crossbar with significantly improved throughput and energy efficiency.

Chapter Contents:

• 7.1 Introduction
• 7.2 Deep neural network with quantization
• 7.2.1 Basics of ResNet
• 7.2.2 Quantized convolution and residual block
• 7.2.3 Quantized BN
• 7.2.4 Quantized activation function and pooling
• 7.2.5 Quantized deep neural network overview
• 7.2.6 Training strategy
• 7.3 Device for in-memory computing
• 7.3.1 ReRAM crossbar
• 7.3.2 Customized DAC and ADC circuits
• 7.3.3 In-memory computing architecture
• 7.4 Quantized ResNet on ReRAM crossbar
• 7.4.1 Mapping strategy
• 7.4.2 Overall architecture
• 7.5 Experiment result
• 7.5.1 Experiment settings
• 7.5.2 Device simulations
• 7.5.3 Accuracy analysis
• 7.5.3.1 Peak accuracy comparison
• 7.5.3.2 Accuracy under device variation
• 7.5.3.3 Accuracy under approximation
• 7.5.4 Performance analysis
• 7.5.4.1 Energy and area
• 7.5.4.2 Throughput and efficiency

Inspec keywords: convolutional neural nets

Other keywords: ImageNet benchmark; accelerator; energy efficiency; ReRAM crossbar; quantized large-scale ResNet-50 network

Subjects: Neural nets