6 citations · 16 across the 9 of their papers we have counts for
9 papers
ADA-GP: Accelerating DNN Training By Adaptive Gradient Prediction
Vahid Janfaza, Shantanu Mandal, Farabi Mahmud +1
Neural network training is inherently sequential where the layers finish the forward propagation in succession, followed by the calculation and back-propagation of gradients (based…
Large Language Models Based Automatic Synthesis of Software Specifications
Shantanu Mandal, Adhrik Chethan, Vahid Janfaza +5
Software configurations play a crucial role in determining the behavior of software systems. In order to ensure safe and error-free operation, it is necessary to identify the corre…
Synthesizing Programs with Continuous Optimization
Shantanu Mandal, Todd A. Anderson, Javier Turek +2
Automatic software generation based on some specification is known as program synthesis. Most existing approaches formulate program synthesis as a search problem with discrete para…
Attack of the Knights: A Non Uniform Cache Side-Channel Attack
Farabi Mahmud, Sungkeun Kim, Harpreet Singh Chawla +3
For a distributed last-level cache (LLC) in a large multicore chip, the access time to one LLC bank can significantly differ from that to another due to the difference in physical…
MERCURY: Accelerating DNN Training By Exploiting Input Similarity
Vahid Janfaza, Kevin Weston, Moein Razavi +4
Deep Neural Networks (DNN) are computationally intensive to train. It consists of a large number of multidimensional dot products between many weights and input vectors. However, t…
Continual Learning Approach for Improving the Data and Computation Mapping in Near-Memory Processing System
Pritam Majumder, Jiayi Huang, Sungkeun Kim +4
The resurgence of near-memory processing (NMP) with the advent of big data has shifted the computation paradigm from processor-centric to memory-centric computing. To meet the band…
The Case for Learning Application Behavior to Improve Hardware Energy Efficiency
Kevin Weston, Vahid Jafanza, Arnav Kansal +3
Computer applications are continuously evolving. However, significant knowledge can be harvested from a set of applications and applied in the context of unknown applications. In t…
Learning Fitness Functions for Machine Programming
Shantanu Mandal, Todd A. Anderson, Javier S. Turek +3
The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genet…
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Mejbah Alam, Justin Gottschlich, Nesime Tatbul +3
The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of…