PiC-BNN: A 128-kbit 65 nm Processing-in-CAM-Based End-to-End Binary Neural Network Accelerator
This work addresses energy and area limitations in BNN accelerators for edge computing applications, though it is incremental as it builds on existing BNN and CAM-based methods.
The authors tackled the inefficiency of typical Binary Neural Networks (BNNs) that still use full-precision operations for non-linear layers by proposing PiC-BNN, an end-to-end binary accelerator using Hamming distance tolerance in Content Addressable Memory, achieving 95.2% accuracy on MNIST and 93.5% on the Hand Gesture dataset with a throughput of 560K inferences/s and power efficiency of 703M inferences/s/W.
Binary Neural Networks (BNNs), where weights and activations are constrained to binary values (+1, -1), are a highly efficient alternative to traditional neural networks. Unfortunately, typical BNNs, while binarizing linear layers (matrix-vector multiplication), still implement other network layers (batch normalization, softmax, output layer, and sometimes the input layer of a convolutional neural network) in full precision. This limits the area and energy benefits and requires architectural support for full precision operations. We propose PiC-BNN, a true end-to-end binary in-approximate search (Hamming distance tolerant) Content Addressable Memory based BNN accelerator. PiC-BNN is designed and manufactured in a commercial 65nm process. PiC-BNN uses Hamming distance tolerance to apply the law of large numbers to enable accurate classification without implementing full precision operations. PiC-BNN achieves baseline software accuracy (95.2%) on the MNIST dataset and 93.5% on the Hand Gesture (HG) dataset, a throughput of 560K inferences/s, and presents a power efficiency of 703M inferences/s/W when implementing a binary MLP model for MNIST/HG dataset classification.