80 research outputs found
LUNA-CIM: Lookup Table based Programmable Neural Processing in Memory
This paper presents a novel approach for performing computations using
Look-Up Tables (LUTs) tailored specifically for Compute-in-Memory applications.
The aim is to address the scalability challenges associated with LUT-based
computation by reducing storage requirements and energy consumption while
capitalizing on the faster and more energy-efficient nature of look-up methods
compared to conventional mathematical computations. The proposed method
leverages a divide and conquer (D&C) strategy to enhance the scalability of
LUT-based computation. By breaking down high-precision multiplications into
lower-precision operations, the technique achieves significantly lower area
overheads, up to approximately 3.7 times less than conventional LUT-based
approaches, without compromising accuracy. To validate the effectiveness of the
proposed method, extensive simulations using TSMC 65 nm technology were
conducted. The experimental analysis reveals that the proposed approach
accounts for less than 0.1\% of the total energy consumption, with only a 32\%
increase in area overhead. These results demonstrate considerable improvements
achieved in energy efficiency and area utilization through the novel
low-energy, low-area-overhead LUT-based computation in an SRAM array
DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection
Securing critical assets in a bus-based System-On-Chip (SoC) is imperative to
mitigate potential vulnerabilities and prevent unauthorized access, ensuring
the integrity, availability, and confidentiality of the system. Ensuring
security throughout the SoC design process is a formidable task owing to the
inherent intricacies in SoC designs and the dispersion of assets across diverse
IPs. Large Language Models (LLMs), exemplified by ChatGPT (OpenAI) and BARD
(Google), have showcased remarkable proficiency across various domains,
including security vulnerability detection and prevention in SoC designs. In
this work, we propose DIVAS, a novel framework that leverages the knowledge
base of LLMs to identify security vulnerabilities from user-defined SoC
specifications, map them to the relevant Common Weakness Enumerations (CWEs),
followed by the generation of equivalent assertions, and employ security
measures through enforcement of security policies. The proposed framework is
implemented using multiple ChatGPT and BARD models, and their performance was
analyzed while generating relevant CWEs from the SoC specifications provided.
The experimental results obtained from open-source SoC benchmarks demonstrate
the efficacy of our proposed framework.Comment: 15 pages, 7 figures, 8 table
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