tag: inference

GDDR6 Memory On The Leading Edge
By Frank Ferro - 12 Aug, 2021 - Comments: 0

With the accelerating growth in data traffic, it is unsurprising that the number of hyperscale data centers keeps rocketing skyward. According to analysts at the Synergy Research Group, in nine months (Q2’20 to Q1’21), 84 new hyperscale data centers came online bringing the total worldwide to 625. Hyperscaler capex set a record $150B over the last four quarters eclipsing the $121B spent in ... » read more

RaPiD: AI Accelerator for Ultra-low Precision Training and Inference
By Technical Paper Link - 02 Jul, 2021 - Comments: 0

Abstract—"The growing prevalence and computational demands of Artificial Intelligence (AI) workloads has led to widespread use of hardware accelerators in their execution. Scaling the performance of AI accelerators across generations is pivotal to their success in commercial deployments. The intrinsic error-resilient nature of AI workloads present a unique opportunity for performance/energy i... » read more

Challenges Of Edge AI Inference
By Vinay Mehta - 01 Jul, 2021 - Comments: 0

Bringing convolutional neural networks (CNNs) to your industry—whether it be medical imaging, robotics, or some other vision application entirely—has the potential to enable new functionalities and reduce the compute requirements for existing workloads. This is because a single CNN can replace more computationally expensive image processing, denoising, and object detection algorithms. Howev... » read more

Challenges In Developing A New Inferencing Chip
By Ed Sperling - 01 Jul, 2021 - Comments: 0

Cheng Wang, co-founder and senior vice president of software and engineering at Flex Logix, sat down with Semiconductor Engineering to explain the process of bringing an inferencing accelerator chip to market, from bring-up, programming and partitioning to tradeoffs involving speed and customization.   SE: Edge inferencing chips are just starting to come to market. What challenges di... » read more

Architectural Considerations For AI
By Brian Bailey - 24 Jun, 2021 - Comments: 2

Custom chips, labeled as artificial intelligence (AI) or machine learning (ML), are appearing on a weekly basis, each claiming to be 10X faster than existing devices or consume 1/10 the power. Whether that is enough to dethrone existing architectures, such as GPUs and FPGAs, or whether they will survive alongside those architectures isn't clear yet. The problem, or the opportunity, is that t... » 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

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

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

The Best AI Edge Inference Benchmark
By Dana McCarty - 04 Mar, 2021 - Comments: 0

When evaluating the performance of an AI accelerator, there’s a range of methodologies available to you. In this article, we’ll discuss some of the different ways to structure your benchmark research before moving forward with an evaluation that directly runs your own model. Just like when buying a car, research will only get you so far before you need to get behind the wheel and give your ... » read more

Tapping Into Purpose-Built Neural Network Models For Even Bigger Efficiency Gains
By Quenton Hall - 09 Dec, 2020 - Comments: 0

Neural networks can be categorized as a set of algorithms modelled loosely after the human brain that can ‘learn’ by incorporating new data. Indeed, many benefits can be derived from developing purpose-built “computationally efficient” neural network models. However, to ensure your model is effective, there are several key requirements that need to be considered. One critical conside... » read more

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