AI’s Impact On Power And Performance


AI/ML is creeping into everything these days. There are AI chips, and there are chips that include elements of AI, particularly for inferencing. The big question is how well they will affect performance and power, and the answer isn't obvious. There are two main phases of AI, the training and the inferencing. Almost all training is done in the cloud using extremely large data sets. In fact, ... » read more

AI’s Blind Spots


The rush to utilize AI/ML in nearly everything and everywhere raises some serious questions about how all of this technology will evolve, age and perform over time. AI is very useful at doing certain tasks, notably finding patterns and relationships in broad data sets that are well beyond the capabilities of the human mind. This is very valuable for adding efficiency into processes of all so... » read more

Visually Assisted Layout In Custom Design


Avina Verma, group director for R&D in Synopsys’ Design Group, explains why visual feedback and graphical guidance are so critical in complex layouts, particularly for mixed-signal environments. » read more

Using HLS To Improve Algorithms


Can an HLS optimization tool outperform expert-level hand-optimizations? A recently published white paper examines how SLX FPGA is used to optimize a secure hash algorithm. T the results are compared to a competition-winning hand-optimized HLS implementation of the same algorithm. This approach provides a nearly 400x speed-up over the unoptimized implementation and even outperforms the hand ... » read more

Making Sense Of ML Metrics


Steve Roddy, vice president of products for Arm’s Machine Learning Group, talks with Semiconductor Engineering about what different metrics actually mean, and why they can vary by individual applications and use cases. » read more

How Hardware Can Bias AI Data


Clean data is essential to good results in AI and machine learning, but data can become biased and less accurate at multiple stages in its lifetime—from moment it is generated all the way through to when it is processed—and it can happen in ways that are not always obvious and often difficult to discern. Blatant data corruption produces erroneous results that are relatively easy to ident... » read more

Power Is Limiting Machine Learning Deployments


The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many ... » read more

How To Improve ML Power/Performance


Raymond Nijssen, vice president and chief technologist at Achronix, talks about the shift from brute-force performance to more power efficiency in machine learning processing, the new focus on enough memory bandwidth to keep MAC functions busy, and how dynamic range, precision and locality can be modified to improve speed and reduce power. » read more

5G Design Changes


Mike Fitton, senior director of strategic planning at Achronix, talks with Semiconductor Engineering about the two distinct parts of 5G deployment, how to get a huge amount of data from the core to the edge of a device where it is usable, and how a network on chip can improve the flow of data. » read more

Algorithms And Security


From a security standpoint, the best thing AI has going for it is that it's in a state of perpetual change. That also may be the worst thing. The problem, at least for now, is that no one knows for sure. What’s clear is that security is not a primary concern when it comes to designing and building AI systems. In many cases it’s not even an action item because architectures are constantly... » read more

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