Search Results for author: Ravi Iyer

Found 11 papers, 0 papers with code

LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!

no code implementations23 Aug 2024 Jainaveen Sundaram, Ravi Iyer

Multimodal Large Language Models (MM-LLMs) have seen significant advancements in the last year, demonstrating impressive performance across tasks.

Mem-Rec: Memory Efficient Recommendation System using Alternative Representation

no code implementations12 May 2023 Gopi Krishna Jha, Anthony Thomas, Nilesh Jain, Sameh Gobriel, Tajana Rosing, Ravi Iyer

Deep learning-based recommendation systems (e. g., DLRMs) are widely used AI models to provide high-quality personalized recommendations.

Recommendation Systems

RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments

no code implementations10 Apr 2023 Drew Penney, Bin Li, Lizhong Chen, Jaroslaw J. Sydir, Anna Drewek-Ossowicka, Ramesh Illikkal, Charlie Tai, Ravi Iyer, Andrew Herdrich

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership.

Streaming Encoding Algorithms for Scalable Hyperdimensional Computing

no code implementations20 Sep 2022 Anthony Thomas, Behnam Khaleghi, Gopi Krishna Jha, Sanjoy Dasgupta, Nageen Himayat, Ravi Iyer, Nilesh Jain, Tajana Rosing

Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience.

EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring

no code implementations15 Sep 2022 Yash Akhauri, J. Pablo Munoz, Nilesh Jain, Ravi Iyer

Our methodology efficiently discovers an interpretable and generalizable zero-cost proxy that gives state of the art score-accuracy correlation on all datasets and search spaces of NASBench-201 and Network Design Spaces (NDS).

Neural Architecture Search

Mitigating Sampling Bias and Improving Robustness in Active Learning

no code implementations13 Sep 2021 Ranganath Krishnan, Alok Sinha, Nilesh Ahuja, Mahesh Subedar, Omesh Tickoo, Ravi Iyer

This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.

Active Learning

Robust Contrastive Active Learning with Feature-guided Query Strategies

no code implementations13 Sep 2021 Ranganath Krishnan, Nilesh Ahuja, Alok Sinha, Mahesh Subedar, Omesh Tickoo, Ravi Iyer

We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations.

Active Learning Image Classification +1

RHNAS: Realizable Hardware and Neural Architecture Search

no code implementations17 Jun 2021 Yash Akhauri, Adithya Niranjan, J. Pablo Muñoz, Suvadeep Banerjee, Abhijit Davare, Pasquale Cocchini, Anton A. Sorokin, Ravi Iyer, Nilesh Jain

The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficiency and address productivity challenges.

Neural Architecture Search

ML-driven Malware that Targets AV Safety

no code implementations24 Apr 2020 Saurabh Jha, Shengkun Cui, Subho S. Banerjee, Timothy Tsai, Zbigniew Kalbarczyk, Ravi Iyer

Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption.

Autonomous Driving

Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks

no code implementations9 May 2018 Charles Eckert, Xiaowei Wang, Jingcheng Wang, Arun Subramaniyan, Ravi Iyer, Dennis Sylvester, David Blaauw, Reetuparna Das

This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.

Quantization

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