Chip-chat: Challenges and opportunities in conversational hardware design
Modern hardware design starts with specifications provided in natural language. These are
then translated by hardware engineers into appropriate Hardware Description Languages …
then translated by hardware engineers into appropriate Hardware Description Languages …
Artificial intelligence for safety-critical systems in industrial and transportation domains: A survey
Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …
Rtllm: An open-source benchmark for design rtl generation with large language model
Inspired by the recent success of large language models (LLMs) like ChatGPT, researchers
start to explore the adoption of LLMs for agile hardware design, such as generating design …
start to explore the adoption of LLMs for agile hardware design, such as generating design …
A survey of reasoning with foundation models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …
[KNJIGA][B] VLSI physical design: from graph partitioning to timing closure
The electronic design automation (EDA) industry develops software to support engineers in
the creation of new integrated circuit (IC) designs. Due to the high complexity of modern …
the creation of new integrated circuit (IC) designs. Due to the high complexity of modern …
A hierarchical adaptive multi-task reinforcement learning framework for multiplier circuit design
Multiplier design---which aims to explore a large combinatorial design space to
simultaneously optimize multiple conflicting objectives---is a fundamental problem in the …
simultaneously optimize multiple conflicting objectives---is a fundamental problem in the …
A survey of graph neural networks for electronic design automation
Driven by Moore's law, the chip design complexity is steadily increasing. Electronic Design
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Robust GNN-based representation learning for HLS
The efficient and timely optimization of microarchitecture for a target application is hindered
by the long evaluation runtime of a design candidate, creating a serious burden. To tackle …
by the long evaluation runtime of a design candidate, creating a serious burden. To tackle …
Circuitnet: An open-source dataset for machine learning in vlsi cad applications with improved domain-specific evaluation metric and learning strategies
The design automation community has been actively exploring machine learning (ML) for
very-large-scale-integrated (VLSI) computer-aided design (CAD). Many studies have …
very-large-scale-integrated (VLSI) computer-aided design (CAD). Many studies have …
Offline model-based optimization via policy-guided gradient search
Offline optimization is an emerging problem in many experimental engineering domains
including protein, drug or aircraft design, where online experimentation to collect evaluation …
including protein, drug or aircraft design, where online experimentation to collect evaluation …