Why It’s So Hard To Secure AI Chips


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


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


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


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


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


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


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


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

Cadence Cerebrus In SaaS And Imagination Technologies Case Study


Artificial Intelligence (AI) has made noteworthy progress and is now ready and available for electronic design automation. The Cadence Cerebrus Intelligent Chip Explorer utilizes AI—specifically, reinforcement machine learning (ML) technology—combined with the industry-leading Cadence digital full flow to deliver better power, performance, and area (PPA) more quickly. However, this highl... » read more

Transformer Model Based Clustering Methodology For Standard Cell Layout Automation (Nvidia)


A new technical paper titled "Novel Transformer Model Based Clustering Method for Standard Cell Design Automation" was published by researchers at Nvidia. Abstract "Standard cells are essential components of modern digital circuit designs. With process technologies advancing beyond 5nm, more routability issues have arisen due to the decreasing number of routing tracks (RTs), increasing numb... » read more

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