tag: machine learning

Neuromorphic Hardware Accelerator For Heterogeneous Many-Accelerator SoCs
By Technical Paper Link - 28 Sep, 2023 - Comments: 0

A technical paper titled “SpikeHard: Efficiency-Driven Neuromorphic Hardware for Heterogeneous Systems-on-Chip” was published by researchers at Columbia University. Abstract: "Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed wi... » read more

Network-on-Chips Enabling Artificial Intelligence/Machine Learning Everywhere
By Frank Schirrmeister - 28 Sep, 2023 - Comments: 0

Recently, I attended the AI HW Summit in Santa Clara and Autosens in Brussels. Artificial intelligence and machine learning (AI/ML) were critical themes for both events, albeit from different angles. While AI/ML as a buzzword is very popular these days in all its good and bad ways, in discussions with customers and prospects, it became clear that we need to be precise in defining what type of A... » read more

Thoughts On AI Consciousness
By Anda Ioana Enescu Buyruk - 28 Sep, 2023 - Comments: 0

By Anda Ioana Enescu Buyruk and Catalin Tudor The rapid advancement of artificial intelligence (AI) has sparked profound discussions regarding the possibility of AI systems achieving consciousness. Such a development carries immense implications, forcing us to redirect our focus from studying the behavior of other organisms to scrutinizing ourselves. This article will delve into the concept ... » read more

When And Where To Implement AI/ML In Fabs
By Anne Meixner - 21 Sep, 2023 - Comments: 0

Deciphering complex interactions between variables is where machine learning and deep learning shine, but figuring out exactly how ML-based systems will be most useful is the job of engineers. The challenge is in pairing their domain expertise with available ML tools to maximize the value of both. This depends on sufficient quantities of good data, highly optimized algorithms, and proper tra... » read more

Photonic-Electronic SmartNIC With Fast and Energy-Efficient Photonic Computing Cores (MIT)
By Technical Paper Link - 19 Sep, 2023 - Comments: 0

A technical paper titled “Lightning: A Reconfigurable Photonic-Electronic SmartNIC for Fast and Energy-Efficient Inference” was published by researchers at Massachusetts Institute of Technology (MIT). Abstract: "The massive growth of machine learning-based applications and the end of Moore's law have created a pressing need to redesign computing platforms. We propose Lightning, the first ... » read more

Automotive Intrusion Detection Methodologies (TU Denmark)
By Technical Paper Link - 06 Sep, 2023 - Comments: 0

A new technical paper titled "Intrusion Detection in the Automotive Domain: A Comprehensive Review" was published by researchers at DTU Compute Technical University of Denmark Abstract "The automotive domain has realized amazing advancements in communication, connectivity, and automation— and at a breakneck pace. Such advancements come with ample benefits, such as the reduction of traffic... » read more

Tradeoffs In DSP Design
By Ed Sperling - 07 Aug, 2023 - Comments: 0

More intelligence is now required in the front-, mid-, and back-haul for 5G/6G communication, requiring a mix of high performance, low power, and enough flexibility to accommodate constantly changing protocols and algorithms. One solution to these conflicting goals involves reconfigurable DSPs, in which the processing element is hardwired like an ASIC but still configurable for a variety of app... » read more

A Survey Of Machine Learning Applications In Functional Verification
By Siemens EDA - 02 Aug, 2023 - Comments: 0

Functional verification is computationally and data-intensive by nature, making it a natural target of machine learning applications. This paper provides a comprehensive and up-to-date analysis of FV problems addressable by ML. Among the various ML techniques and algorithms, several emerging ones have demonstrated outstanding potential in FV. Yet despite the promising research results, criti... » read more

Using AI To Close Coverage Gaps
By Ed Sperling - 26 Jul, 2023 - Comments: 0

Verification of complex, heterogeneous chips is becoming much more difficult and time-consuming. There are more corner cases, and devices have to last longer and behave according to spec throughout their lifetimes. This is where AI fits in. It can help identify redundancy and provide information about why a particular device or block may not be able to be fully covered, and it can do it in less... » read more

Using ML For Improved Fab Scheduling
By Katherine Derbyshire - 25 Jul, 2023 - Comments: 0

Expanding fab capacity is slow and expensive even under ideal circumstances. It has been still more difficult in recent years, as pandemic-related shortages have strained equipment supply chains. When integrated circuit demand rises faster than expansions can fill the gap, fabs try to find “hidden” capacity through improved operations. They hope that more efficient workflows will allow e... » read more

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