tag: Quadric

In Memory, At Memory, Near Memory: What Would Goldilocks Choose?
By Steve Roddy - 17 Oct, 2024 - Comments: 0

The children’s fairy tale of ‘Goldilocks and the Three Bears’ describes the adventures of Goldi as she tries to choose among three choices for bedding, chairs, and bowls of porridge. One meal is “too hot,” the other “too cold,” and finally one is “just right.” If Goldi were faced with making architecture choices for AI processing in modern edge/device SoCs, she would also face... » read more

Mass Customization For AI Inference
By Ann Mutschler - 17 Oct, 2024 - Comments: 0

Rising complexity in AI models and an explosion in the number and variety of networks is leaving chipmakers torn between fixed-function acceleration and more programmable accelerators, and creating some novel approaches that include some of both. By all accounts, a general-purpose approach to AI processing is not meeting the grade. General-purpose processors are exactly that. They're not des... » read more

Using AI To Glue Disparate IC Ecosystem Data
By Ed Sperling and Ann Mutschler - 26 Sep, 2024 - Comments: 0

AI holds the potential to change how companies interact throughout the global semiconductor ecosystem, gluing together different data types and processes that can be shared between companies that in the past had little or no direct connections. Chipmakers always have used abstraction layers to see the bigger picture of how the various components of a chip go together, allowing them to pinpoi... » read more

Can You Rely Upon Your NPU Vendor To Be Your Customers’ Data Science Team?
By Steve Roddy - 12 Sep, 2024 - Comments: 0

The biggest mistake a chip design team can make in evaluating AI acceleration options for a new SoC is to rely entirely upon spreadsheets of performance numbers from the NPU vendor without going through the exercise of porting one or more new machine learning networks themselves using the vendor toolsets. Why is this a huge red flag? Most NPU vendors tell prospective customers that (1) the v... » read more

Balancing Programmability And Performance In Cars
By Ann Mutschler - 05 Sep, 2024 - Comments: 0

The rate of change in the automotive industry is accelerating with the shift toward software-defined vehicles and ongoing advancements in algorithms and chip architectures. The challenge now is to figure out the best way to prevent rapid obsolescence, improve safety, and keep the cost of these changes to a minimum. Today, updatable automotive hardware is typically achieved through FPGAs, but... » read more

AI’s Role In Chip Design Widens, Drawing In New Startups
By Karen Heyman - 29 Aug, 2024 - Comments: 0

Using AI in EDA is reinvigorating the whole tools industry, prompting established players to upgrade their tool offerings with AI/ML features, while drawing in startups trying to carve out differentiated approaches to fill unaddressed gaps with new tools and methodologies. Today’s new generation of entrepreneurs is comprised of both young post-grads with innovative ideas and industry veter... » read more

CPU Performance Bottlenecks Limit Parallel Processing Speedups
By Bryon Moyer - 12 Aug, 2024 - Comments: 0

Multi-core processors theoretically can run many threads of code in parallel, but some categories of operation currently bog down attempts to raise overall performance by parallelizing computing. Is it time to have accelerators for running highly parallel code? Standard processors have many CPUs, so it follows that cache coherency and synchronization can involve thousands of cycles of low-le... » read more

ConvNext Runs 28X Faster Than Fallback
By Steve Roddy - 22 Jul, 2024 - Comments: 0

Two months ago in our blog we highlighted the fallacy of using a conventional NPU accelerator paired with a DSP or CPU for “fallback” operations. (Fallback Fails Spectacularly, May 2024). In that blog we calculated what the expected performance would be for a system with a DSP needing to perform the new operations found in one of today’s leading new ML networks – ConvNext. The result wa... » read more

IC Power Optimization Required, But More Difficult To Achieve
By Adam Kovac - 18 Jul, 2024 - Comments: 0

Power optimization is playing an increasingly vital role in chip and chip and system designs, but it's also becoming much harder to achieve as transistor density and system complexity continue to grow. This is especially evident with advanced packages, chiplets, and high-performance chips, all of which are becoming more common in complex designs. Inside data centers, racks of servers are str... » read more

IC Industry’s Growing Role In Sustainability
By Ann Mutschler - 17 Jun, 2024 - Comments: 1

The massive power needs of AI systems are putting a spotlight on sustainability in the semiconductor ecosystem. The chip industry needs to be able to produce more efficient and lower-power semiconductors. But demands for increased processing speed are rising with the widespread use of large language models and the overall increase in the amount of data that needs to be processed. Gartner estima... » read more

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