78 citations · 109 across the 12 of their papers we have counts for
13 papers
Automated Multi-Agent Workflows for RTL Design
Amulya Bhattaram, Janani Ramamoorthy, Ranit Gupta +2
The rise of agentic AI workflows unlocks novel opportunities for computer systems design and optimization. However, for specialized domains such as program synthesis, the relative…
GeoFlow: Agentic Workflow Automation for Geospatial Tasks
Amulya Bhattaram, Justin Chung, Stanley Chung +5
We present GeoFlow, a method that automatically generates agentic workflows for geospatial tasks. Unlike prior work that focuses on reasoning decomposition and leaves API selection…
Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows
Dimitrios Stamoulis, Diana Marculescu
Geospatial Copilots hold immense potential for automating Earth observation (EO) and climate monitoring workflows, yet their reliance on large-scale models such as GPT-4o introduce…
Stable Diffusion For Aerial Object Detection
Yanan Jian, Fuxun Yu, Simranjit Singh +1
Aerial object detection is a challenging task, in which one major obstacle lies in the limitations of large-scale data collection and the long-tail distribution of certain classes.…
Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination
Fuxun Yu, Zirui Xu, Tong Shen +12
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. A…
Third ArchEdge Workshop: Exploring the Design Space of Efficient Deep Neural Networks
Fuxun Yu, Dimitrios Stamoulis, Di Wang +2
This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs). Specifically, we cover two aspects: (1) static architectu…
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardw…
Single-Path NAS: Device-Aware Efficient ConvNet Design
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the latency constraint of a mobile device? Neural Architecture Se…
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device? Neural architecture se…
Hardware-Aware Machine Learning: Modeling and Optimization
Diana Marculescu, Dimitrios Stamoulis, Ermao Cai
Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system. The increased popularity of DL applications dep…
Designing Adaptive Neural Networks for Energy-Constrained Image Classification
Dimitrios Stamoulis, Ting-Wu Chin, Anand Krishnan Prakash +4
As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on e…
HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networks
Dimitrios Stamoulis, Ermao Cai, Da-Cheng Juan +1
While selecting the hyper-parameters of Neural Networks (NNs) has been so far treated as an art, the emergence of more complex, deeper architectures poses increasingly more challen…
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks
Ermao Cai, Da-Cheng Juan, Dimitrios Stamoulis +1
"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from…