79 citations · 262 across the 25 of their papers we have counts for
26 papers
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantizati…
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a de…
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems
Ahmet Inci, Evgeny Bolotin, Yaosheng Fu +4
With deep reinforcement learning (RL) methods achieving results that exceed human capabilities in games, robotics, and simulated environments, continued scaling of RL training is c…
One Weight Bitwidth to Rule Them All
Ting-Wu Chin, Pierce I-Jen Chuang, Vikas Chandra +1
Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applicatio…
Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond
R. Iris Bahar, Alex K. Jones, Srinivas Katkoori +3
Integrated circuits and electronic systems, as well as design technologies, are evolving at a great rate -- both quantitatively and qualitatively. Major developments include new in…
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
Zhuo Chen, Jiyuan Zhang, Ruizhou Ding +1
In recent years, Convolutional Neural Networks (CNNs) have shown superior capability in visual learning tasks. While accuracy-wise CNNs provide unprecedented performance, they are…