tag: LLMs

Introducing An Agentic LLM For Chip Design
By Tanay Biradar - 25 Jun, 2026 - Comments: 0

By Tanay Biradar, Surya Gunukula, Tengxiao Liu, and Kexun Zhang ChipAgents has introduced Renoir, an agentic large language model (LLM) whose name means "renew." In early chip design benchmarks, Renoir outperforms the base model it was trained on and cuts costs by more than half. Furthermore, it can run entirely on-premises, allowing semiconductor companies to develop faster without compromi... » read more

Beyond The Demo: Deploying And Evaluating Open-Source AI Workloads
By Odin Shen - 11 Jun, 2026 - Comments: 0

As more open-source AI models move closer to real-world adoption, developers are changing how they evaluate edge deployment. The question is no longer simply whether a model can run, but whether it can be deployed reproducibly on a concrete platform, observed in practice, and turned into meaningful deployment decisions based on actual technical evidence. For developers, the CIX Armv9 platfor... » read more

Building Multi-Agent Systems For ASIC Flows
By Ed Sperling - 10 Jun, 2026 - Comments: 0

If one AI agent can solve a problem in a certain amount of time, can multiple agents solve it faster? The answer is yes, but only if the agents have well-defined roles and targets. This is where orchestrators fit in, and why they are so critical to agentic AI. Kexun Zhang, head of research at ChipAgents, talks about what exactly AI agents are, how they can be used to solve big problems that wou... » read more

The Edge LLM Offload Story
By Karthikeyan Shanmuga Vadivel - 04 Jun, 2026 - Comments: 0

By Karthikeyan Shanmuga Vadivel and Sauryadeep Pal Developers and system architects today face a growing demand to enable large language model variants on device. They are facing pressure to support transformer-capable models on constrained devices to ensure data privacy, eliminate cloud API charges, and provide offline reliability. On-device execution is also becoming a necessity to meet st... » read more

Toward Agentic Verification
By Brian Bailey - 28 May, 2026 - Comments: 1

Key Takeaways: Agentic verification provides flow orchestration for common repetitive tasks. Capabilities will expand when tools can learn from a larger context, including the specification. Design houses need to fully understand the costs and benefits and plan accordingly. Agentic verification is more than a buzzword. It is a pivotal moment in the evolution of verification ... » read more

Faster Verification Debug With AI
By Taruna Reddy - 28 May, 2026 - Comments: 0

Every stage of semiconductor development takes longer and requires more effort with each new generation of chips. At no stage is this more apparent than functional verification. Industry consensus is that verification consumes roughly two-thirds of development time and resources. Within verification, debug is the most challenging step, consuming a third to two-thirds of the effort. Any serious ... » read more

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

Large-scale, SRAM-based LLM Inference Deployment (Groq)
By Technical Paper Link - 21 May, 2026 - Comments: 0

A new technical paper, "SHIP: SRAM-Based Huge Inference Pipelines for Fast LLM Serving," was published by researchers at Nvidia, with work done while at Groq. Abstract "The proliferation of large language models (LLMs) demands inference systems with both low latency and high efficiency at scale. GPU-based serving relies on HBM for model weights and KV caches, creating a memory bandwidth b... » read more

Why Vision LLMs Force A Rethink Of Edge AI Hardware
By Athish Rao - 14 May, 2026 - Comments: 0

As vision-centric large language models move on-device, performance measured in raw TOPS is no longer enough. Architectures need to be built around real workloads, memory behavior, and sustained utilization, especially at the edge. Vision LLMs are changing the edge AI equation For the last decade, most edge AI silicon has been built to do one job extremely well: run convolutional networks for... » read more

Silent Data Corruption: A Major Reliability Challenge in Large-Scale LLM Training (TU Berlin)
By Technical Paper Link - 12 Apr, 2026 - Comments: 0

A new technical paper, "Exploring Silent Data Corruption as a Reliability Challenge in LLM Training," was published by researchers at Technische Universitat Berlin. Abstract "As Large Language Models (LLMs) scale in size and complexity, the consequences of failures during training become increasingly severe. A major challenge arises from Silent Data Corruption (SDC): hardware-induced faults... » read more

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