50 citations · 111 across the 9 of their papers we have counts for
10 papers
Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference
Ruokai Yin, Sattwik Deb Mishra, Xuan Zuo +3
Distributed LLM inference requires careful coordination of parallelization strategies across hundreds to thousands of NPUs to meet production SLOs. Current systems like Megatron-LM…
UDC: Unified DNAS for Compressible TinyML Models
Igor Fedorov, Ramon Matas, Hokchhay Tann +3
Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity. Neural processing unit (NPU) hardware address the memory challenge by u…
A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks
Hokchhay Tann, Heng Zhao, Sherief Reda
Applications of Fully Convolutional Networks (FCN) in iris segmentation have shown promising advances. For mobile and embedded systems, a significant challenge is that the proposed…
Principles of Information Storage in Small-Molecule Mixtures
Jacob K. Rosenstein, Christopher Rose, Sherief Reda +11
Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this…
Parallelized Linear Classification with Volumetric Chemical Perceptrons
Christopher E. Arcadia, Hokchhay Tann, Amanda Dombroski +7
In this work, we introduce a new type of linear classifier that is implemented in a chemical form. We propose a novel encoding technique which simultaneously represents multiple da…
BLASYS: Approximate Logic Synthesis Using Boolean Matrix Factorization
Soheil Hashemi, Hokchhay Tann, Sherief Reda
Approximate computing is an emerging paradigm where design accuracy can be traded off for benefits in design metrics such as design area, power consumption or circuit complexity. I…
Flexible Deep Neural Network Processing
Hokchhay Tann, Soheil Hashemi, Sherief Reda
The recent success of Deep Neural Networks (DNNs) has drastically improved the state of the art for many application domains. While achieving high accuracy performance, deploying s…
Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks
Hokchhay Tann, Soheil Hashemi, Iris Bahar +1
While Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning applications, they often require millions of expensive floating-point operations for each input…
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks
Soheil Hashemi, Nicholas Anthony, Hokchhay Tann +2
Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same…
Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off
Hokchhay Tann, Soheil Hashemi, R. Iris Bahar +1
We present a novel dynamic configuration technique for deep neural networks that permits step-wise energy-accuracy trade-offs during runtime. Our configuration technique adjusts th…