PIMBALL: Binary Neural Networks in Spintronic Memory
arXiv:1812.03989
Abstract
Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency or hardware/software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU, GPU, and FPGA based implementations while delivering higher throughput.
References in corpus (7)
- Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
- DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
- Compressing Neural Networks with the Hashing Trick
- XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference
- Tunable damping, saturation magnetization, and exchange stiffness of half-Heusler NiMnSb thin films
- Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA
- Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks