6 citations · 11 across the 4 of their papers we have counts for
4 papers
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach
Yequan Zhao, Hai Li, Ian Young +1
Back propagation (BP) is the default solution for gradient computation in neural network training. However, implementing BP-based training on various edge devices such as FPGA, mic…
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Yequan Zhao, Xian Xiao, Xinling Yu +5
Solving partial differential equations (PDEs) numerically often requires huge computing time, energy cost, and hardware resources in practical applications. This has limited their…
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks
Yequan Zhao, Xinling Yu, Zhixiong Chen +3
Backward propagation (BP) is widely used to compute the gradients in neural network training. However, it is hard to implement BP on edge devices due to the lack of hardware and so…
Tensorized Optical Multimodal Fusion Network
Yequan Zhao, Xian Xiao, Geza Kurczveil +2
We propose the first tensorized optical multimodal fusion network architecture with a self-attention mechanism and low-rank tensor fusion. Simulation results show les…