tag: algorithms

Things That Go Bump In The Daytime
By Ed Sperling - 14 Jan, 2020 - Comments: 0

There is no argument that autonomous technology is better at certain things than systems controlled by people. A computer-guided system has only one mission — to stay on the road, avoid object, and reach the end destination. It doesn't get tired, text, or look out the window. And it can park within a millimeter of a wall or another vehicle without hitting it, and do that every time — as lon... » read more

What Will AI Look Like In 10 Years?
By Ed Sperling - 19 Dec, 2019 - Comments: 0

There's no such thing as reverse in AI systems. Once they are let loose, they do what they were programmed to do — optimize results within a given set of parameters. But today there is no consistency for those parameters. There are no standards by which to measure how AI deviates over time. And there is an expectation, at least today, that AI systems will adapt to whatever patterns they di... » read more

AI’s Impact On Power And Performance
By Ed Sperling - 14 Nov, 2019 - Comments: 0

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
By Ed Sperling - 12 Nov, 2019 - Comments: 1

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
By Ed Sperling - 29 Oct, 2019 - Comments: 0

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
By Zubair Wadood - 24 Oct, 2019 - Comments: 0

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
By Ed Sperling - 16 Oct, 2019 - Comments: 0

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
By Ed Sperling - 10 Sep, 2019 - Comments: 1

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
By Brian Bailey - 25 Jul, 2019 - Comments: 3

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
By Ed Sperling - 12 Jul, 2019 - Comments: 0

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

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