tag: machine learning

KAN: Kolmogorov Arnold Networks: An Alternative To MLPs (MIT, CalTech, et al.)
By Technical Paper Link - 13 Jun, 2024 - Comments: 0

A new technical paper titled "KAN: Kolmogorov-Arnold Networks" was published by researchers at MIT, CalTech, Northeastern University and The NSF Institute for Artificial Intelligence and Fundamental Interactions. Abstract: "Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While... » read more

KANs Explode!
By Steve Roddy - 13 Jun, 2024 - Comments: 0

In late April 2024, a novel AI research paper was published by researchers from MIT and CalTech proposing a fundamentally new approach to machine learning networks – the Kolmogorov Arnold Network – or KAN. In the six weeks since its publication, the AI research field is ablaze with excitement and speculation that KANs might be a breakthrough that dramatically alters the trajectory of AI mod... » read more

Why It’s So Hard To Secure AI Chips
By Adam Kovac - 06 Jun, 2024 - Comments: 0

Demand for high-performance chips designed specifically for AI applications is spiking, driven by massive interest in generative AI at the edge and in the data center, but the rapid growth in this sector also is raising concerns about the security of these devices and the data they process. Generative AI — whether it's OpenAI’s ChatGPT, Anthropic’s Claude, or xAI’s Grok — sifts thr... » read more

MCU Changes At The Edge
By Ed Sperling - 03 Jun, 2024 - Comments: 0

Microcontrollers are becoming a key platform for processing machine learning at the edge due to two significant changes. First, they now can include multiple cores, including some for high performance and others for low power, as well as other specialized processing elements such as neural network accelerators. Second, machine learning algorithms have been pruned to the point where inferencing ... » read more

AI For Data Management
By Adam Kovac - 30 May, 2024 - Comments: 0

Data management is becoming a significant new challenge for the chip industry, as well as a brand new opportunity, as the amount of data collected at every step of design through manufacturing continues to grow. Exacerbating the problem is the rising complexity of designs, many of which are highly customized and domain-specific at the leading edge, as well as increasing demands for reliabili... » read more

Hardware Fuzzer Utilizing LLMs
By Technical Paper Link - 12 Apr, 2024 - Comments: 0

A new technical paper titled "Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing" was published by researchers at TU Darmstadt and Texas A&M University. Abstract "Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware ... » read more

High-NA EUVL: Automated Defect Inspection Based on SEMI-SuperYOLO-NAS
By Technical Paper Link - 10 Apr, 2024 - Comments: 0

A new technical paper titled "Towards Improved Semiconductor Defect Inspection for high-NA EUVL based on SEMI-SuperYOLO-NAS" was published by researchers at KU Leuven, imec, Ghent University, and SCREEN SPE. Abstract "Due to potential pitch reduction, the semiconductor industry is adopting High-NA EUVL technology. However, its low depth of focus presents challenges for High Volume Manufac... » read more

New Strategies For Interpreting Data Variability
By Gregory Haley - 09 Apr, 2024 - Comments: 0

Every measurement counts at the nanoscopic scale of modern semiconductor processes, but with each new process node the number of measurements and the need for accuracy escalate dramatically. Petabytes of new data are being generated and used in every aspect of the manufacturing process for informed decision-making, process optimization, and the continuous pursuit of quality and yield. Most f... » read more

Faster And Better Floorplanning With ML-Based Macro Placement
By Synopsys - 03 Apr, 2024 - Comments: 0

The chips contained in today’s consumer and commercial electronic products are staggering in size and complexity. The largest devices include central processing units (CPUs), graphics processing units (GPUs), and system-on-chip (SoC) devices that integrate many functions on a single die. Additionally, chips are expanding beyond their traditional borders with multi-die approaches such as 2.5DI... » read more

Ultimate Guide To Machine Learning For Embedded Systems
By Renesas - 03 Apr, 2024 - Comments: 0

Machine learning is a subfield of artificial intelligence which gives computers an ability to learn from data in an iterative manner using different techniques. Our aim here being to learn and predict from data. This is a big diversion from other fields which poses the limitation of programming instructions instead of learning from them. Machine learning in embedded systems specifically target ... » read more

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