tag: neural networks

Getting Better Edge Performance & Efficiency From Acceleration-Aware ML Model Design
By Vlad Bronstein - 04 Nov, 2021 - Comments: 0

The advent of machine learning techniques has benefited greatly from the use of acceleration technology such as GPUs, TPUs and FPGAs. Indeed, without the use of acceleration technology, it’s likely that machine learning would have remained in the province of academia and not had the impact that it is having in our world today. Clearly, machine learning has become an important tool for solving... » 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

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

Why Reconfigurability Is Essential For AI Edge Inference Throughput
By Vinay Mehta - 06 May, 2021 - Comments: 0

For a neural network to run at its fastest, the underlying hardware must run efficiently on all layers. Through the inference of any CNN—whether it be based on an architecture such as YOLO, ResNet, or Inception—the workload regularly shifts from being bottlenecked by memory to being bottlenecked by compute resources. You can think of each convolutional layer as its own mini-workload, and so... » read more

Developers Turn To Analog For Neural Nets
By Bryon Moyer - 06 May, 2021 - Comments: 1

Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Power/Performance Bits: May 4
By Jesse Allen - 04 May, 2021 - Comments: 0

Speculative execution vulnerable again Computer scientists from the University of Virginia and University of California San Diego warn of a processor architecture vulnerability that gets around the techniques used to secure processors in the wake of Spectre. In 2018, Spectre and the similar Meltdown vulnerability were announced. These types of attacks could allow malicious agents to exploit... » read more

Applications, Challenges For Using AI In Fabs
By Mark LaPedus - 14 Apr, 2021 - Comments: 0

Experts at the Table: Semiconductor Engineering sat down to discuss chip scaling, transistors, new architectures, and packaging with Jerry Chen, head of global business development for manufacturing & industrials at Nvidia; David Fried, vice president of computational products at Lam Research; Mark Shirey, vice president of marketing and applications at KLA; and Aki Fujimura, CEO of D2S. Wh... » read more

How Do Machines Learn?
By Weiyang (Will) Zhou - 06 Apr, 2021 - Comments: 0

We depend, or hope to depend, on machines, especially computers, to do many things, from organizing our photos to parking our cars. Machines are becoming less and less "mechanical" and more and more "intelligent." Machine learning has become a familiar phrase to many people in advanced manufacturing. The next natural question people may ask is: How do machines learn? Recognizing diverse obje... » read more

Maximizing Edge AI Performance
By Vinay Mehta - 01 Apr, 2021 - Comments: 0

Inference of convolutional neural network models is algorithmically straightforward, but to get the fastest performance for your application there are a few pitfalls to keep in mind when deploying. A number of factors make efficient inference difficult, which we will first step through before diving into specific solutions to address and resolve each. By the end of this article, you will be arm... » read more

Power/Performance Bits: March 16
By Jesse Allen - 16 Mar, 2021 - Comments: 0

Adaptable neural nets Neural networks go through two phases: training, when weights are set based on a dataset, and inference, when new information is assessed based on those weights. But researchers at MIT, Institute of Science and Technology Austria, and Vienna University of Technology propose a new type of neural network that can learn during inference and adjust its underlying equations to... » read more

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