Susmita Dey Manasi

About Susmita Dey Manasi

Susmita Dey Manasi, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at University of Minnesota-Twin Cities, specializes in the field of Deep learning accelerator, VLSI design automation, Approximate computing.

His recent articles reflect a diverse array of research interests and contributions to the field:

An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

Reusing GEMM hardware for efficient execution of depthwise separable convolution on ASIC-based DNN accelerators

A Unified Engine for Accelerating GNN Weighting/Aggregation Operations, with Efficient Load Balancing and Graph-Specific Caching

Physically accurate learning-based performance prediction of hardware-accelerated ml algorithms

GNNIE: GNN inference engine with load-balancing and graph-specific caching

Efficient Computation of Deep Neural Workloads on Domain-Specific Custom Accelerator Platforms

VeriGOOD-ML: An open-source flow for automated ML hardware synthesis

Susmita Dey Manasi Information

University

Position

___

Citations(all)

100

Citations(since 2020)

93

Cited By

24

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

5

i10Index(since 2020)

5

Email

University Profile Page

Google Scholar

Susmita Dey Manasi Skills & Research Interests

Deep learning accelerator

VLSI design automation

Approximate computing

Top articles of Susmita Dey Manasi

An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators

arXiv preprint arXiv:2308.12120

2023/8/23

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

arXiv preprint arXiv:2306.16767

2023/6/29

Reusing GEMM hardware for efficient execution of depthwise separable convolution on ASIC-based DNN accelerators

2023/1/16

Susmita Dey Manasi
Susmita Dey Manasi

H-Index: 2

A Unified Engine for Accelerating GNN Weighting/Aggregation Operations, with Efficient Load Balancing and Graph-Specific Caching

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

2022/12/27

Physically accurate learning-based performance prediction of hardware-accelerated ml algorithms

2022/9/12

GNNIE: GNN inference engine with load-balancing and graph-specific caching

2022/7/10

Efficient Computation of Deep Neural Workloads on Domain-Specific Custom Accelerator Platforms

2022

Susmita Dey Manasi
Susmita Dey Manasi

H-Index: 2

VeriGOOD-ML: An open-source flow for automated ML hardware synthesis

2021/11/1

Fast and efficient constraint evaluation of analog layout using machine learning models

2021/1/18

DeepOpt: Optimized scheduling of CNN workloads for ASIC-based systolic deep learning accelerators

2021/1/18

Susmita Dey Manasi
Susmita Dey Manasi

H-Index: 2

SeFAct2: Selective feature activation for energy-efficient CNNs using optimized thresholds

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

2020/8/13

NeuPart: Using analytical models to drive energy-efficient partitioning of CNN computations on cloud-connected mobile clients

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

2020/6/10

Susmita Dey Manasi
Susmita Dey Manasi

H-Index: 2

Farhana Sharmin Snigdha
Farhana Sharmin Snigdha

H-Index: 4

See List of Professors in Susmita Dey Manasi University(University of Minnesota-Twin Cities)

Co-Authors

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