53 citations · 65 across the 5 of their papers we have counts for
11 papers
Compiling Halide Programs to Push-Memory Accelerators
Qiaoyi Liu, Dillon Huff, Jeff Setter +8
Image processing and machine learning applications benefit tremendously from hardware acceleration, but existing compilers target either FPGAs, which sacrifice power and performanc…
Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective
Jian-Guo Liu, Ziheng Wang, Yantong Xie +2
In the mean field integrate-and-fire model, the dynamics of a typical neuron within a large network is modeled as a diffusion-jump stochastic process whose jump takes place once th…
SparseDNN: Fast Sparse Deep Learning Inference on CPUs
Ziheng Wang
The last few years have seen gigantic leaps in algorithms and systems to support efficient deep learning inference. Pruning and quantization algorithms can now consistently compres…
SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference
Ziheng Wang
In recent years, there has been a flurry of research in deep neural network pruning and compression. Early approaches prune weights individually. However, it is difficult to take a…
Structured Pruning of Large Language Models
Ziheng Wang, Jeremy Wohlwend, Tao Lei
Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency hav…
Accelerated CNN Training Through Gradient Approximation
Ziheng Wang, Sree Harsha Nelaturu
Training deep convolutional neural networks such as VGG and ResNet by gradient descent is an expensive exercise requiring specialized hardware such as GPUs. Recent works have exami…