32 research outputs found
VerilogEval: Evaluating Large Language Models for Verilog Code Generation
The increasing popularity of large language models (LLMs) has paved the way
for their application in diverse domains. This paper proposes a benchmarking
framework tailored specifically for evaluating LLM performance in the context
of Verilog code generation for hardware design and verification. We present a
comprehensive evaluation dataset consisting of 156 problems from the Verilog
instructional website HDLBits. The evaluation set consists of a diverse set of
Verilog code generation tasks, ranging from simple combinational circuits to
complex finite state machines. The Verilog code completions can be
automatically tested for functional correctness by comparing the transient
simulation outputs of the generated design with a golden solution. We also
demonstrate that the Verilog code generation capability of pretrained language
models could be improved with supervised fine-tuning by bootstrapping with LLM
generated synthetic problem-code pairs.Comment: ICCAD 2023 Invited Pape
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
IR drop is a fundamental constraint required by almost all chip designs.
However, its evaluation usually takes a long time that hinders mitigation
techniques for fixing its violations. In this work, we develop a fast dynamic
IR drop estimation technique, named PowerNet, based on a convolutional neural
network (CNN). It can handle both vector-based and vectorless IR analyses.
Moreover, the proposed CNN model is general and transferable to different
designs. This is in contrast to most existing machine learning (ML) approaches,
where a model is applicable only to a specific design. Experimental results
show that PowerNet outperforms the latest ML method by 9% in accuracy for the
challenging case of vectorless IR drop and achieves a 30 times speedup compared
to an accurate IR drop commercial tool. Further, a mitigation tool guided by
PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs,
respectively, with very limited modification on their power grids
TAG: Learning Circuit Spatial Embedding From Layouts
Analog and mixed-signal (AMS) circuit designs still rely on human design
expertise. Machine learning has been assisting circuit design automation by
replacing human experience with artificial intelligence. This paper presents
TAG, a new paradigm of learning the circuit representation from layouts
leveraging text, self-attention and graph. The embedding network model learns
spatial information without manual labeling. We introduce text embedding and a
self-attention mechanism to AMS circuit learning. Experimental results
demonstrate the ability to predict layout distances between instances with
industrial FinFET technology benchmarks. The effectiveness of the circuit
representation is verified by showing the transferability to three other
learning tasks with limited data in the case studies: layout matching
prediction, wirelength estimation, and net parasitic capacitance prediction.Comment: Accepted by ICCAD 202
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Design flow parameters are of utmost importance to chip design quality and
require a painfully long time to evaluate their effects. In reality, flow
parameter tuning is usually performed manually based on designers' experience
in an ad hoc manner. In this work, we introduce a machine learning-based
automatic parameter tuning methodology that aims to find the best design
quality with a limited number of trials. Instead of merely plugging in machine
learning engines, we develop clustering and approximate sampling techniques for
improving tuning efficiency. The feature extraction in this method can reuse
knowledge from prior designs. Furthermore, we leverage a state-of-the-art
XGBoost model and propose a novel dynamic tree technique to overcome
overfitting. Experimental results on benchmark circuits show that our approach
achieves 25% improvement in design quality or 37% reduction in sampling cost
compared to random forest method, which is the kernel of a highly cited
previous work. Our approach is further validated on two industrial designs. By
sampling less than 0.02% of possible parameter sets, it reduces area by 1.83%
and 1.43% compared to the best solutions hand-tuned by experienced designers
The Concise guide to pharmacology 2019/20: Ion channels
The Concise Guide to PHARMACOLOGY 2019/20 is the fourth in this series of biennial publications. The Concise Guide provides concise overviews of the key properties of nearly 1800 human drug targets with an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide represents approximately 400 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point‐in‐time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.14749. Ion channels are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein‐coupled receptors, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid‐2019, and supersedes data presented in the 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC‐IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate
Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)
In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field
THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: Ion channels
The Concise Guide to PHARMACOLOGY 2021/22 is the fifth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of nearly 1900 human drug targets with an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes over 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point‐in‐time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/bph.15539. Ion channels are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein‐coupled receptors, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid‐2021, and supersedes data presented in the 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC‐IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate
The Concise Guide to PHARMACOLOGY 2023/24: Ion channels
The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and over 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org/), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point‐in‐time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16178. Ion channels are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein‐coupled receptors, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid‐2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC‐IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate