1 citations · 3 across the 4 of their papers we have counts for
3 papers · 1 filter
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation
Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi +1
Transfer learning has become a popular task adaptation method in the era of foundation models. However, many foundation models require large storage and computing resources, which…
Training-Free Acceleration of ViTs with Delayed Spatial Merging
Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi +1
Token merging has emerged as a new paradigm that can accelerate the inference of Vision Transformers (ViTs) without any retraining or fine-tuning. To push the frontier of training-…
Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators
Jung Hwan Heo, Arash Fayyazi, Amirhossein Esmaili +1
This paper introduces the sparse periodic systolic (SPS) dataflow, which advances the state-of-the-art hardware accelerator for supporting lightweight neural networks. Specifically…