tag: retrieval-augmented generation

The Shape Of Prompts: Exploring Their Effect On Inference Infrastructure
By Amritam Putatunda - 28 May, 2026 - Comments: 0

AI inference prompts exhibit a shape-shifting behavior, arriving in many forms and attempting to fit themselves within the constraints of the inference stack. Ultimately, it is the design of the inference infrastructure that determines whether it can sustain a large volume of prompts or only a limited number. Prompts are not uniform transactions; they represent dynamic workload profiles whose ... » read more

Rethinking The Role Of CPUs In AI: A Practical RAG Implementation
By Odin Shen - 11 Dec, 2025 - Comments: 0

In many enterprise environments, engineers and technical staff need to find information quickly. They search internal documents such as hardware specifications, project manuals, and technical notes. These materials are often scattered, making traditional search inefficient. These documents are often confidential or proprietary. This constraint prevents these documents from being processed by... » read more

AI Agents For UVM Generation: Challenges And Opportunities
By Kexun Zhang - 25 Sep, 2025 - Comments: 0

By Yuheng Tang and Kexun Zhang In the last two years, the role of AI tools in developers' workflows has rapidly expanded. What were once simple "code completion" engines have since evolved into agents that can read documentation, test their own code, and improve via self-reflection. While AI has already begun enhancing RTL design workflows, its exploration in verification remains in early st... » read more

Performance And Energy Characterization Of A Commercial Compute-in-SRAM Device (Cornell, USC, MIT, GSI)
By Technical Paper Link - 16 Sep, 2025 - Comments: 0

A new technical paper titled "Characterizing and Optimizing Realistic Workloads on a Commercial Compute-in-SRAM Device" was published by researchers at Cornell University, USC, MIT and GSI Technology Inc. Abstract "Compute-in-SRAM architectures offer a promising approach to achieving higher performance and energy efficiency across a range of data-intensive applications. However, prior evalu... » read more

Reliable Training Data Paramount To AI Model Success
By Ann Mutschler - 14 Aug, 2025 - Comments: 0

AI systems are increasingly being integrated into safety- and mission-critical applications ranging from automotive to health care and industrial IoT, stepping up the need for training data that is reliable, secure, and which is generated from trusted sources. AI activity is growing exponentially, as everybody tries to figure out how to apply it to their domain, application, or workload. In ... » read more

HW Security: Multi-Agent AI Assistant Leveraging LLMs To Automate Key Stages of SoC Security Verification (U. of Florida)
By Technical Paper Link - 25 Jun, 2025 - Comments: 0

A new technical paper titled "SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models" was published by researchers at University of Florida. Abstract "Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, c... » read more

LLM-based Agentic Framework Automating HW Security Threat Modeling And Test Plan Generation (U. of Florida)
By Technical Paper Link - 01 Apr, 2025 - Comments: 0

A new technical paper titled "ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification" was published by researchers at University of Florida. Abstract "Current hardware security verification processes predominantly rely on manual threat modeling and test plan generation, which are labor-intensive, error-prone, and struggle to scale with increasing ... » read more

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