Zobrazeno 1 - 10
of 671
pro vyhledávání: '"KARRI, RAMESH"'
Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, of
Externí odkaz:
http://arxiv.org/abs/2412.03220
Autor:
Xiao, Weihua, Putrevu, Venkata Sai Charan, Hemadri, Raghu Vamshi, Garg, Siddharth, Karri, Ramesh
Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with minimized area and delay is crucial for enhancing the performance of
Externí odkaz:
http://arxiv.org/abs/2412.02594
High-Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for automatically refactoring C code into HLS-compatible formats. We
Externí odkaz:
http://arxiv.org/abs/2412.00214
Autor:
Mankali, Lakshmi Likhitha, Bhandari, Jitendra, Alam, Manaar, Karri, Ramesh, Maniatakos, Michail, Sinanoglu, Ozgur, Knechtel, Johann
Large language models (LLMs) have demonstrated remarkable potential with code generation/completion tasks for hardware design. In fact, LLM-based hardware description language (HDL) code generation has enabled the industry to realize complex designs
Externí odkaz:
http://arxiv.org/abs/2411.17569
Masala-CHAI is the first fully automated framework leveraging large language models (LLMs) to generate Simulation Programs with Integrated Circuit Emphasis (SPICE) netlists. It addresses a long-standing challenge in automating netlist generation for
Externí odkaz:
http://arxiv.org/abs/2411.14299
Autor:
Blocklove, Jason, Thakur, Shailja, Tan, Benjamin, Pearce, Hammond, Garg, Siddharth, Karri, Ramesh
Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are emerging as a
Externí odkaz:
http://arxiv.org/abs/2411.11856
Autor:
Abramovich, Talor, Udeshi, Meet, Shao, Minghao, Lieret, Kilian, Xi, Haoran, Milner, Kimberly, Jancheska, Sofija, Yang, John, Jimenez, Carlos E., Khorrami, Farshad, Krishnamurthy, Prashanth, Dolan-Gavitt, Brendan, Shafique, Muhammad, Narasimhan, Karthik, Karri, Ramesh, Press, Ofir
Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain. We present EnIGMA, an LM agent for
Externí odkaz:
http://arxiv.org/abs/2409.16165
Robots interacting with humans through natural language can unlock numerous applications such as Referring Grasp Synthesis (RGS). Given a text query, RGS determines a stable grasp pose to manipulate the referred object in the robot's workspace. RGS c
Externí odkaz:
http://arxiv.org/abs/2409.10419
Large Language Models (LLMs) are effective in computer hardware synthesis via hardware description language (HDL) generation. However, LLM-assisted approaches for HDL generation struggle when handling complex tasks. We introduce a suite of hierarchic
Externí odkaz:
http://arxiv.org/abs/2407.18276
Autor:
Bhandari, Jitendra, Sadhukhan, Rajat, Krishnamurthy, Prashanth, Khorrami, Farshad, Karri, Ramesh
A globally distributed IC supply chain brings risks due to untrusted third parties. The risks span inadvertent use of hardware Trojan (HT), inserted Intellectual Property (3P-IP) or Electronic Design Automation (EDA) flows. HT can introduce stealthy
Externí odkaz:
http://arxiv.org/abs/2407.12352