Accurate Cost Estimation of Memory Systems Inspired by Machine Learning for Computer Vision

Servadei, Lorenzo; Zennaro, Elena; Devarajegowda, Keerthikumara; Manzinger, Martin; Ecker, Wolfgang; Wille, Robert

doi:10.23919/date.2019.8714961

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI and machine learning (ML) approaches are increasingly being integrated into hardware design processes, providing a fresh approach to addressing various phases and layers of abstraction. By estimating hardware overhead [101], optimizing logic [102], routing [103], and introducing test points [104], these techniques address scalability difficulties and accelerate design completion. Using AI and ML in hardware design enables better optimization, more efficiency, and shorter development cycles.…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption Challenges

Nyako,

Devkota,

et al. 2023

Electronics

View full text Add to dashboard Cite

The field of microelectronics has experienced extensive integration into various aspects of our everyday lives, evident via its utilization across a wide range of devices such as cellphones, airplanes, computers, wristwatches, and other similar technologies. Microelectronics are vital to the healthcare and defense industries, making them vulnerable to counterfeit products. Currently, the complicated global microelectronics supply chain involves the production of varied components in multiple places, resulting in tremendous risk. In this scenario, it is possible for hostile or adversarial actors to exploit the situation by intentionally introducing counterfeit components. This hostile behavior could steal data or use these components as remote kill switches. To address these problems, enormous resources are being committed to research, innovation, and development to build trust in microelectronics. This research study provides a thorough analysis of the taxonomy associated with prominent attack, detection, and avoidance models in the realm of counterfeit microelectronics. This research aims to improve our understanding of dependable microelectronics. Prevention strategies like Physical Unclonable Functions (PUFs) and machine learning (ML), and detection methods like aging-based fingerprints are reviewed in this study. Finally, we underscore the significance of interdisciplinary cooperation, commitment to norms, and proactive methods.

show abstract

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption Challenges

Nyako,

Devkota,

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…ML techniques, including DL have shown promising results across numerous applications, including across the CAD domain. Recent work spans the design flow, from early-stage hardware cost estimations [14], through logic synthesis [20], and physical design [7]. We explore the use of transfer learning [10] to teach a DL-based model to produce Verilog by framing it as a machine translation problem.…”

Section: Background and Related Workmentioning

confidence: 99%

“…As such, there is an opportunity for automatic translation to increase productivity and reduce the burdens on human designers. Given successful adoption of Machine Learning (ML) throughout the Integrated Circuit (IC) Computer-Aided Design (CAD) flow (e.g, [7,14,20]), we are motivated to investigate if state-of-the-art ML can help in even earlier design stages.…”

Section: Introductionmentioning

confidence: 99%

DAVE: Deriving Automatically Verilog from English

Pearce

Tan

Karri

2020

Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD

View full text Add to dashboard Cite

While specifications for digital systems are provided in natural language, engineers undertake significant efforts to translate them into the programming languages understood by compilers for digital systems. Automating this process allows designers to work with the language in which they are most comfortable -the original natural language-and focus instead on other downstream design challenges. We explore the use of state-of-the-art machine learning (ML) to automatically derive Verilog snippets from English via finetuning GPT-2, a natural language ML system. We describe our approach for producing a suitable dataset of novice-level digital design tasks and provide a detailed exploration of GPT-2, finding encouraging translation performance across our task sets (94.8 % correct), with the ability to handle both simple and abstract design tasks.

show abstract