ML Automotive Chip Design Takes Off


Machine learning is increasingly being deployed across a wide swath of chips and electronics in automobiles, both for improving reliability of standard parts and for the creation of extremely complex AI chips used in increasingly autonomous applications. On the design side, the majority of EDA tools today rely on reinforcement learning, a machine learning subset of AI that teaches a machine ... » read more

Role Of IoT Software Expanding


IoT software is becoming much more sophisticated and complex as vendors seek to optimize it for specific applications, and far more essential for vendors looking to deliver devices on-time and on-budget across multiple market segments. That complexity varies widely across the IoT. For example, the sensor monitoring for a simple sprinkler system is far different than the preventive maintenanc... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition


If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

From Data Center To End Device: AI/ML Inference With GDDR6


Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inference. As inference migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be incre... » read more

ML-Based Third-Party IP Trust Verification Framework (U. of Florida, U. of Kansas)


A technical paper titled "Hardware IP Assurance against Trojan Attacks with Machine Learning and Post-processing" was published by researchers at University of Florida and University of Kansas. Abstract: "System-on-chip (SoC) developers increasingly rely on pre-verified hardware intellectual property (IP) blocks often acquired from untrusted third-party vendors. These IPs might contain hidd... » read more

AI Adoption Slow For Design Tools


A lot of excitement, and a fair amount of hype, surrounds what artificial intelligence (AI) can do for the EDA industry. But many challenges must be overcome before AI can start designing, verifying, and implementing chips for us. Should AI replace the algorithms in use today, or does it have a different role to play? At the end of the day, AI is a technique that has strengths and weaknesses... » read more

EDA Makes A Frenzied Push Into Machine Learning


Machine learning is becoming a competitive prerequisite for the EDA industry. Big chipmakers are endorsing and demanding it, and most EDA companies are deploying it for one or more steps in the design flow, with plans to add much more over time. In recent weeks, the three largest EDA vendors have made sweeping announcements about incorporating ML into their tools at their respective user eve... » read more

Hyperscale HW Optimized Neural Architecture Search (Google)


A new technical paper titled "Hyperscale Hardware Optimized Neural Architecture Search" was published by researchers at Google, Apple, and Waymo. "This paper introduces the first Hyperscale Hardware Optimized Neural Architecture Search (H2O-NAS) to automatically design accurate and performant machine learning models tailored to the underlying hardware architecture. H2O-NAS consists of three ... » read more

100G Ethernet At The Edge


The amount of data is growing, and so is the need to process it closer to the source. The edge is a middle ground between the cloud and the end point, close enough to where data is generated to reduce the time it takes to process that data, yet still powerful enough to analyze that data quickly and send it wherever it is needed. But to make this all work requires faster conduits for that data i... » read more

How Chip Engineers Plan To Use AI


Experts at the Table: Semiconductor Engineering sat down to discuss how AI is being used today and how engineers expect to use it in the future, with Michael Jackson, corporate vice president for R&D at Cadence; Joel Sumner, vice president of semiconductor and electronics engineering at National Instruments; Grace Yu, product and engineering manager at Meta; and David Pan, professor in the ... » read more

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