A Compilation Flow for the Generation of CNN Inference Accelerators on FPGAs

no code implementations • 8 Mar 2022 • Seung-Hun Chung, Tarek S. Abdelrahman

We improve the quality of the generated hardware with optimizations applied to the base OpenCL kernels generated by TVM.

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

no code implementations • 29 Dec 2019 • Lifu Zhang, Tarek S. Abdelrahman

We use 4 CNNs (LeNet-5, AlexNet, VGG and ResNet) and show that when pipelining is limited to early layers in a network, training with stale weights converges and results in models with comparable inference accuracies to those resulting from non-pipelined training on MNIST and CIFAR-10 datasets; a drop in accuracy of 0. 4%, 4%, 0. 83% and 1. 45% for the 4 networks, respectively.

Fast On-the-fly Retraining-free Sparsification of Convolutional Neural Networks

no code implementations • NIPS Workshop CDNNRIA 2018 • Amir H. Ashouri, Tarek S. Abdelrahman, Alwyn Dos Remedios

Our methods are applied on-the-fly and require no retraining.