DeepSeek: Improving Language Model Reasoning Capabilities Using Pure Reinforcement Learning

By Technical Paper Link - 27 Jan, 2025 - Comments: 0

A new technical paper titled "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning" was published by DeepSeek. Abstract: "We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates rema... » read more

Geometric-Aware Model Merging Approach To Enhance Instruction Alignment in Chip LLMs (Nvidia)

By Technical Paper Link - 15 Jan, 2025 - Comments: 0

A new technical paper titled "ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation" was published by researchers at NVIDIA Research. Abstract: "Recent advancements in large language models (LLMs) have expanded their application across various domains, including chip design, where domain-adapted chip models like ChipNeMo have emerged. However, ... » read more

AI Won’t Replace Subject Matter Experts

By Adam Kovac - 15 Jan, 2025 - Comments: 0

Experts at The Table: The emergence of LLMs and other forms of AI has sent ripples through a number of industries, raising fears that many jobs could be on the chopping block, to be replaced by automation. Whether that’s the case in semiconductors, where machine learning has become an integral part of the design process, remains to be seen. Semiconductor Engineering sat down with a panel of e... » read more

NPU Acceleration For Multimodal LLMs

By Pat Donnelly - 12 Dec, 2024 - Comments: 0

Transformer-based models have rapidly spread from text to speech, vision, and other modalities. This has created challenges for the development of Neural Processing Units (NPUs). NPUs must now efficiently support the computation of weights and propagation of activations through a series of attention blocks. Increasingly, NPUs must be able to process models with multiple input modalities with ac... » read more

Slow Progress On Generative EDA

By Brian Bailey - 26 Nov, 2024 - Comments: 0

Progress is being made in generative EDA, but the lack of training data remains the biggest problem. Some areas are finding ways around this. Generative AI, driven by large language models (LLMs), stormed into the world just two years ago, and since then has worked its way into almost every aspect of our lives. Some people love it, others hate it, and some even give dire warnings about machi... » read more

How AI Is Transforming System Design

By Adam Kovac - 26 Nov, 2024 - Comments: 0

Experts At The Table: ChatGPT and other LLMs have attracted most of the attention in recent years, but other forms of AI have long been incorporated into design workflows. The technology has become so common that many designers may not even realize it’s a part of the tools they use every day. But its adoption is spreading deeper into tools and methodologies. Semiconductor Engineering sat down... » read more

Small Language Models: A Solution To Language Model Deployment At The Edge?

By Paul Karazuba - 18 Nov, 2024 - Comments: 0

While Large Language Models (LLMs) like GPT-3 and GPT-4 have quickly become synonymous with AI, LLM mass deployments in both training and inference applications have, to date, been predominately cloud-based. This is primarily due to the sheer size of the models; the resulting processing and memory requirements often overwhelm the capabilities of edge-based systems. While the efficiency of Exped... » read more

New AI Data Types Emerge

By Bryon Moyer - 14 Nov, 2024 - Comments: 0

AI is all about data, and the representation of the data matters strongly. But after focusing primarily on 8-bit integers and 32‑bit floating-point numbers, the industry is now looking at new formats. There is no single best type for every situation, because the choice depends on the type of AI model, whether accuracy, performance, or power is prioritized, and where the computing happens, ... » read more

Yield Management Embraces Expanding Role

By Laura Peters - 12 Nov, 2024 - Comments: 0

Competitive pressures, shrinking time-to-market windows, and increased customization are collectively changing the dynamics and demands for yield management systems, shifting left from the fab to the design flow and right to assembly, packaging, and in-field analysis. The basic role of yield management systems is still expediting new product introductions, reducing scrap, and delivering grea... » read more

Benchmark and Evaluation Framework For Characterizing LLM Performance In Formal Verification (UC Berkeley, Nvidia)

By Technical Paper Link - 04 Nov, 2024 - Comments: 0

A new technical paper titled "FVEval: Understanding Language Model Capabilities in Formal Verification of Digital Hardware" was published by researchers at UC Berkeley and NVIDIA. Abstract "The remarkable reasoning and code generation capabilities of large language models (LLMs) have spurred significant interest in applying LLMs to enable task automation in digital chip design. In particula... » read more

← Older posts

tag: LLMs

DeepSeek: Improving Language Model Reasoning Capabilities Using Pure Reinforcement Learning

Geometric-Aware Model Merging Approach To Enhance Instruction Alignment in Chip LLMs (Nvidia)

AI Won’t Replace Subject Matter Experts

NPU Acceleration For Multimodal LLMs

Slow Progress On Generative EDA

How AI Is Transforming System Design

Small Language Models: A Solution To Language Model Deployment At The Edge?

New AI Data Types Emerge

Yield Management Embraces Expanding Role

Benchmark and Evaluation Framework For Characterizing LLM Performance In Formal Verification (UC Berkeley, Nvidia)

Trending Articles

Impact of Extremely Low Temperatures On The 5nm SRAM Array Size and Performance

Optimization of Oxygen Plasma Conditions for Cu-Cu Bonding

Global IC Fabs And Facilities Report: 2024

Chip Architectures Becoming Much More Complex With Chiplets