4 citations · 15 across the 10 of their papers we have counts for
10 papers
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng +8
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design…
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng +4
Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist o…
K-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning
Ismail Bustany, Andrew B. Kahng, Ioannis Koutis +2
State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement o…
A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route
Vidya A. Chhabria, Wenjing Jiang, Andrew B. Kahng +1
Due to the unavailability of routing information in design stages prior to detailed routing (DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate…
PROBE3.0: A Systematic Framework for Design-Technology Pathfinding with Improved Design Enablement
Suhyeong Choi, Jinwook Jung, Andrew B. Kahng +4
We propose a systematic framework to conduct design-technology pathfinding for PPAC in advanced nodes. Our goal is to provide configurable, scalable generation of process design ki…
Hier-RTLMP: A Hierarchical Automatic Macro Placer for Large-scale Complex IP Blocks
Andrew B. Kahng, Ravi Varadarajan, Zhiang Wang
In a typical RTL to GDSII flow, floorplanning or macro placement is a critical step in achieving decent quality of results (QoR). Moreover, in today's physical synthesis flows (e.g…