N
Nagarajan Kandasamy
Researcher at Drexel University
Publications - 126
Citations - 3236
Nagarajan Kandasamy is an academic researcher from Drexel University. The author has contributed to research in topics: Neuromorphic engineering & Spiking neural network. The author has an hindex of 25, co-authored 121 publications receiving 2919 citations. Previous affiliations of Nagarajan Kandasamy include Vanderbilt University & University of Michigan.
Papers
More filters
Proceedings ArticleDOI
Adaptive Performance Control of Computing Systems via Distributed Cooperative Control: Application to Power Management in Computing Clusters
TL;DR: A fully decentralized control framework wherein the optimization problem for the system is first decomposed into sub-problems and each sub-problem is solved separately by individual controllers to achieve the overall performance objectives is developed.
Proceedings ArticleDOI
Improving Dependability of Neuromorphic Computing With Non-Volatile Memory
TL;DR: RENEU is proposed, a reliability-oriented approach to map machine learning applications to neuromorphic hardware, with the aim of improving system-wide reliability, without compromising key performance metrics such as execution time of these applications on the hardware.
Journal ArticleDOI
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Adarsha Balaji,Shihao Song,Anup Das,Nikil Dutt,Jeffrey L. Krichmar,Nagarajan Kandasamy,Francky Catthoor +6 more
TL;DR: The proposed framework first extracts the precise times at which a charge pump in the hardware is activated to support neural computations within a workload, then uses a characterized NBTI reliability model to estimate the charge pump's aging during the workload execution.
Proceedings ArticleDOI
A Hierarchical Optimization Framework for Autonomic Performance Management of Distributed Computing Systems
TL;DR: This paper develops a scalable online optimization framework for the autonomic performance management of distributed computing systems operating in a dynamic environment to satisfy desired quality-ofservice objectives and develops the overall control structure.
Proceedings ArticleDOI
Aging-Aware Request Scheduling for Non-Volatile Main Memory
TL;DR: HEBE as discussed by the authors proposes an analytical model that can dynamically track the aging in the peripheral circuitry of each memory bank based on the bank's utilization and develops an intelligent memory request scheduler that exploits this aging model at run time to de-stress the peripheral circuits of a memory bank only when its aging exceeds a critical threshold.