tag: inferencing

How Inferencing Differs From Training in Machine Learning Applications
By Sam Fuller - 06 Jan, 2022 - Comments: 0

Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train a model to enable the machine to learn how to perform a task. ML training is highly iterative with each new piece of training data generating trillions of operations. The iterative nature of the tr... » read more

Using ML In EDA
By Ed Sperling - 20 Sep, 2021 - Comments: 0

Machine learning is becoming essential for designing chips due to the growing volume of data stemming from increasing density and complexity. Nick Ni, director of product marketing for AI at Xilinx, examines why machine learning is gaining traction at advanced nodes, where it’s being used today and how it will be used in the future, how quality of results compare with and without ML, and what... » read more

How Dynamic Hardware Efficiently Solves The Neural Network Complexity Problem
By Sam Fuller - 02 Sep, 2021 - Comments: 0

Given the high computational requirements of neural network models, efficient execution is paramount. When performed trillions of times per second even the tiniest inefficiencies are multiplied into large inefficiencies at the chip and system level. Because AI models continue to expand in complexity and size as they are asked to become more human-like in their (artificial) intelligence, it is c... » read more

Safe And Robust Machine Learning
By Ed Sperling - 15 Jul, 2021 - Comments: 0

Deploying machine learning in the real world is a lot different than developing and testing it in a lab. Quenton Hall, AI systems architect at Xilinx, examines security implications on both the inferencing and training side, the potential for disruptions to accuracy, and how accessible these models and algorithms will be when they are used at the edge and in the cloud. This involves everything ... » read more

Shifting Toward Data-Driven Chip Architectures
By Ed Sperling and Ann Mutschler - 16 Jun, 2021 - Comments: 0

An explosion in data is forcing chipmakers to rethink where to process data, which are the best types of processors and memories for different types of data, and how to structure, partition and prioritize the movement of raw and processed data. New chips from systems companies such as Google, Facebook, Alibaba, and IBM all incorporate this approach. So do those developed by vendors like Appl... » read more

Accelerating AI/ML Inferencing With GDDR6 DRAM
By Frank Ferro - 10 Jun, 2021 - Comments: 0

The origins of graphics double data rate (GDDR) memory can be traced to the rise of 3D gaming on PCs and consoles. The first graphics processing units (GPU) packed single data rate (SDR) and double data rate (DDR) DRAM – the same solution used for CPU main memory. As gaming evolved, the demand for higher frame rates at ever higher resolutions drove the need for a graphics-workload specific me... » read more

There’s More To Machine Learning Than CNNs
By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

Configuring AI Chips
By Ed Sperling - 04 Jun, 2021 - Comments: 0

Change is almost constant in AI systems. Vinay Mehta, technical product marketing manager at Flex Logix, talks about the need for flexible architectures to deal with continual modifications in algorithms, more complex convolutions, and unforeseen system interactions, as well as the ability to apply all of this over longer chip lifetimes. Related Dynamically Reconfiguring Logic A differ... » read more

Making Sense Of New Edge-Inference Architectures
By Bryon Moyer - 04 Mar, 2021 - Comments: 0

New edge-inference machine-learning architectures have been arriving at an astounding rate over the last year. Making sense of them all is a challenge. To begin with, not all ML architectures are alike. One of the complicating factors in understanding the different machine-learning architectures is the nomenclature used to describe them. You’ll see terms like “sea-of-MACs,” “systolic... » read more

Edge-Inference Architectures Proliferate
By Bryon Moyer - 04 Feb, 2021 - Comments: 0

First part of two parts. The second part will dive into basic architectural characteristics. The last year has seen a vast array of announcements of new machine-learning (ML) architectures for edge inference. Unburdened by the need to support training, but tasked with low latency, the devices exhibit extremely varied approaches to ML inference. “Architecture is changing both in the comp... » read more

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