Search Results for author: Ajay Joshi

Found 7 papers, 0 papers with code

Puppeteer: A Random Forest-based Manager for Hardware Prefetchers across the Memory Hierarchy

no code implementations28 Jan 2022 Furkan Eris, Marcia S. Louis, Kubra Eris, Jose L. Abellan, Ajay Joshi

In this work, we propose Puppeteer, which is a hardware prefetcher manager that uses a suite of random forest regressors to determine at runtime which prefetcher should be ON at each level in the memory hierarchy, such that the prefetchers complement each other and we reduce the data/instruction access latency.

Efficient Sealable Protection Keys for RISC-V

no code implementations4 Dec 2020 Leila Delshadtehrani, Sadullah Canakci, Manuel Egele, Ajay Joshi

Recently, Intel introduced a new hardware feature for intra-process memory isolation, called Memory Protection Keys (MPK), which enables a user-space process to switch the domains in an efficient way.

Cryptography and Security Hardware Architecture

Custom Tailored Suite of Random Forests for Prefetcher Adaptation

no code implementations1 Aug 2020 Furkan Eris, Sadullah Canakci, Cansu Demirkiran, Ajay Joshi

To close the gap between memory and processors, and in turn improve performance, there has been an abundance of work in the area of data/instruction prefetcher designs.

CUDA optimized Neural Network predicts blood glucose control from quantified joint mobility and anthropometrics

no code implementations19 Aug 2019 Sterling Ramroach, Andrew Dhanoo, Brian Cockburn, Ajay Joshi

In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++.

The efficacy of various machine learning models for multi-class classification of RNA-seq expression data

no code implementations19 Aug 2019 Sterling Ramroach, Melford John, Ajay Joshi

When the features were reduced to a list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as opposed to the clustering and classification models.

BIG-bench Machine Learning Classification +2

Field of Groves: An Energy-Efficient Random Forest

no code implementations10 Apr 2017 Zafar Takhirov, Joseph Wang, Marcia S. Louis, Venkatesh Saligrama, Ajay Joshi

In this work, we present a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to CNNs and SVMs under tight energy budgets.

General Classification

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