activity
20172022
most citedMitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models

20 citations · 73 across the 8 of their papers we have counts for

collaborators

10 papers

cs.AR2022

Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases

Geraldo F. Oliveira, Amirali Boroumand, Saugata Ghose +2

Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take plac…

cs.AR2022

Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Cooperation

Amirali Boroumand, Saugata Ghose, Geraldo F. Oliveira +1

A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently su…

cs.AR20212 cited

Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks

Amirali Boroumand, Saugata Ghose, Berkin Akin +5

Emerging edge computing platforms often contain machine learning (ML) accelerators that can accelerate inference for a wide range of neural network (NN) models. These models are de…

cs.AR202120 cited

Polynesia: Enabling Effective Hybrid Transactional/Analytical Databases with Specialized Hardware/Software Co-Design

Amirali Boroumand, Saugata Ghose, Geraldo F. Oliveira +1

An exponential growth in data volume, combined with increasing demand for real-time analysis (i.e., using the most recent data), has resulted in the emergence of database systems t…

cs.AR202120 cited

Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models

Amirali Boroumand, Saugata Ghose, Berkin Akin +5

As the need for edge computing grows, many modern consumer devices now contain edge machine learning (ML) accelerators that can compute a wide range of neural network (NN) models w…

cs.AR20202 cited

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl +13

Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. U…