Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D$^2$NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables robust and noise-resilient Multi-task Learning in D$^2$NNs. Our experimental results demonstrate significant improvements in versatility and hardware efficiency, and also demonstrate the robustness of proposed multi-task D$^2$NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.
Pre-Quantized Deep Learning Models Codified in ONNX to Enable Hardware/Software Co-Design
Marvell Technology Inc.（美满科技公司）；Nokia Solutions and Networks Oy（诺基亚解决方案与网络公司）；Nokia of America Corporation（诺基亚美国公司）；Nokia Solutions and Networks Sp. z o.o.（诺基亚解决方案与网络有限责任公司）；Nokia Paris Saclay（诺基亚巴黎萨克雷（此处保留英文原名，因“Paris Saclay”是地名相关，中文常不直译机构名，按原文处理））
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University（南洋理工大学惠普数字制造企业实验室）
