Search Results for author: Ruchir Puri

Found 16 papers, 6 papers with code

Customizing a Large Language Model for VHDL Design of High-Performance Microprocessors

no code implementations14 May 2025 Nicolas Dupuis, Ravi Nair, Shyam Ramji, Sean McClintock, Nishant Chauhan, Priyanka Nagpal, Bart Blaner, Ken Valk, Leon Stok, Ruchir Puri

In this paper, we describe our journey in developing a Large Language Model (LLM) specifically for the purpose of explaining VHDL code, a task that has particular importance in an organization with decades of experience and assets in high-performance processor design.

Language Modeling Language Modelling +1

The infrastructure powering IBM's Gen AI model development

1 code implementation7 Jul 2024 Talia Gershon, Seetharami Seelam, Brian Belgodere, Milton Bonilla, Lan Hoang, Danny Barnett, I-Hsin Chung, Apoorve Mohan, Ming-Hung Chen, Lixiang Luo, Robert Walkup, Constantinos Evangelinos, Shweta Salaria, Marc Dombrowa, Yoonho Park, Apo Kayi, Liran Schour, Alim Alim, Ali Sydney, Pavlos Maniotis, Laurent Schares, Bernard Metzler, Bengi Karacali-Akyamac, Sophia Wen, Tatsuhiro Chiba, Sunyanan Choochotkaew, Takeshi Yoshimura, Claudia Misale, Tonia Elengikal, Kevin O Connor, Zhuoran Liu, Richard Molina, Lars Schneidenbach, James Caden, Christopher Laibinis, Carlos Fonseca, Vasily Tarasov, Swaminathan Sundararaman, Frank Schmuck, Scott Guthridge, Jeremy Cohn, Marc Eshel, Paul Muench, Runyu Liu, William Pointer, Drew Wyskida, Bob Krull, Ray Rose, Brent Wolfe, William Cornejo, John Walter, Colm Malone, Clifford Perucci, Frank Franco, Nigel Hinds, Bob Calio, Pavel Druyan, Robert Kilduff, John Kienle, Connor McStay, Andrew Figueroa, Matthew Connolly, Edie Fost, Gina Roma, Jake Fonseca, Ido Levy, Michele Payne, Ryan Schenkel, Amir Malki, Lion Schneider, Aniruddha Narkhede, Shekeba Moshref, Alexandra Kisin, Olga Dodin, Bill Rippon, Henry Wrieth, John Ganci, Johnny Colino, Donna Habeger-Rose, Rakesh Pandey, Aditya Gidh, Dennis Patterson, Samsuddin Salmani, Rambilas Varma, Rumana Rumana, Shubham Sharma, Aditya Gaur, Mayank Mishra, Rameswar Panda, Aditya Prasad, Matt Stallone, Gaoyuan Zhang, Yikang Shen, David Cox, Ruchir Puri, Dakshi Agrawal, Drew Thorstensen, Joel Belog, Brent Tang, Saurabh Kumar Gupta, Amitabha Biswas, Anup Maheshwari, Eran Gampel, Jason Van Patten, Matthew Runion, Sai Kaki, Yigal Bogin, Brian Reitz, Steve Pritko, Shahan Najam, Surya Nambala, Radhika Chirra, Rick Welp, Frank DiMitri, Felipe Telles, Amilcar Arvelo, King Chu, Ed Seminaro, Andrew Schram, Felix Eickhoff, William Hanson, Eric Mckeever, Michael Light, Dinakaran Joseph, Piyush Chaudhary, Piyush Shivam, Puneet Chaudhary, Wesley Jones, Robert Guthrie, Chris Bostic, Rezaul Islam, Steve Duersch, Wayne Sawdon, John Lewars, Matthew Klos, Michael Spriggs, Bill McMillan, George Gao, Ashish Kamra, Gaurav Singh, Marc Curry, Tushar Katarki, Joe Talerico, Zenghui Shi, Sai Sindhur Malleni, Erwan Gallen

This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks.

model

Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination

no code implementations3 Jun 2024 Luyao Shi, Michael Kazda, Bradley Sears, Nick Shropshire, Ruchir Puri

Electronic design engineers are challenged to find relevant information efficiently for a myriad of tasks within design construction, verification and technology development.

Hallucination Question Answering +2

Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code

no code implementations29 May 2024 Nicolas Dupuis, Luca Buratti, Sanjay Vishwakarma, Aitana Viudes Forrat, David Kremer, Ismael Faro, Ruchir Puri, Juan Cruz-Benito

To evaluate our models, we have developed a custom benchmark, similar to HumanEval, which includes a set of tests specifically designed for the field of quantum computing programming using Qiskit.

HumanEval

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

1 code implementation25 May 2021 Ruchir Puri, David S. Kung, Geert Janssen, Wei zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, Frederick Reiss

In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.

BIG-bench Machine Learning Code Classification +2

Model Agnostic Contrastive Explanations for Structured Data

no code implementations31 May 2019 Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri

Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model.

model

Bias Mitigation Post-processing for Individual and Group Fairness

no code implementations14 Dec 2018 Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R. Varshney, Ruchir Puri

Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness.

Fairness General Classification

IBM Deep Learning Service

2 code implementations18 Sep 2017 Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.

Distributed, Parallel, and Cluster Computing

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