701 citations · 750 across the 4 of their papers we have counts for
4 papers
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen, Jiamin Ni, Songtao Lu +8
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, num…
Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Sungho Shin, Youngmin Jo, Jungwook Choi +3
Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational…
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
Jungwook Choi, Pierce I-Jen Chuang, Zhuo Wang +3
Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. In order to reduce this cost, several quantization schemes have gained…
PACT: Parameterized Clipping Activation for Quantized Neural Networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani +3
Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. To address this cost, a number of quantization schemes have been propo…