Deep Learning Models With MATLAB And Cortex-A


Today, I’ve teamed up with Ram Cherukuri of MathWorks to provide an overview of the MathWorks toolchain for machine learning (ML) and the deployment of embedded ML inference on Arm Cortex-A using the Arm Compute Library. MathWorks enables engineers to get started quickly and makes machine learning possible without having to become an expert. If you’re an algorithm engineer interested ... » read more

Do Large Batches Always Improve Neural Network Throughput?


Common benchmarks like ResNet-50 generally have much higher throughput with large batch sizes than with batch size =1. For example, the Nvidia Tesla T4 has 4x the throughput at batch=32 than when it is processing in batch=1 mode. Of course, larger batch sizes have a tradeoff: latency increases which may be undesirable in real-time applications. Why do larger batches increase throughput... » read more

Machine Learning Drives High-Level Synthesis Boom


High-level synthesis (HLS) is experiencing a new wave of popularity, driven by its ability to handle machine-learning matrices and iterative design efforts. The obvious advantage of HLS is the boost in productivity designers get from working in C, C++ and other high-level languages rather than RTL. The ability to design a layout that should work, and then easily modify it to test other confi... » read more

Multi-Layer Processing Boosts Inference Throughput/Watt


The focus in discussion of inference throughput is often on the computations required. For example, YOLOv3, a power real time object detection and recognition model, requires 227 BILLION MACs (multiply-accumulates) to process a single 2 Mega Pixel image! This is with the Winograd Transformation; it’s more than 300 Billion without it. And there is a lot of discussion of the large size ... » read more

AI: Where’s The Money?


A one-time technology outcast, Artificial Intelligence (AI) has come a long way. Now there’s groundswell of interest and investments in products and technologies to deliver high performance visual recognition, matching or besting human skills. Equally, speech and audio recognition are becoming more common and we’re even starting to see more specialized applications such as finding optimized... » read more

Inference Acceleration: Follow The Memory


Much has been written about the computational complexity of inference acceleration: very large matrix multiplies for fully-connected layers and huge numbers of 3x3 convolutions across megapixel images, both of which require many thousands of MACs (multiplier-accumulators) to achieve high throughput for models like ResNet-50 and YOLOv3. The other side of the coin is managing the movement of d... » read more

The Other Side Of Makimoto’s Wave


Custom hardware is undergoing a huge resurgence across a variety of new applications, pushing the semiconductor industry to the other side of Makimoto's Wave. Tsugio Makimoto, the technologist who identified the chip industry’s 10-year cyclical swings between standardization and customization, predicted there always will be room in ASICs for general-purpose processors. But it's becoming mo... » read more

Pushing AI Into The Mainstream


Artificial intelligence is emerging as the driving force behind many advancements in technology, even though the industry has merely scratched the surface of what may be possible. But how deeply AI penetrates different market segments and technologies, and how quickly it pushes into the mainstream, depend on a variety of issues that still must be resolved. In addition to a plethora of techni... » read more

AI Accelerator Gyrfalcon Soars Post Stealth


Milpitas, Calif.-based startup Gyrfalcon Technology Inc. (GTI), which emerged from semi-stealth mode in September, recently announced the datacenter-focused second generation of its neural-network accelerator, which was first aimed at the endpoint. GTI is not alone: The endpoint market is growing. By 2022, 25% of endpoint devices will execute AI algorithms (inference for neural network appli... » read more

Building AI SoCs


Ron Lowman, strategic marketing manager at Synopsys, looks at where AI is being used and how to develop chips when the algorithms are in a state of almost constant change. That includes what moves to the edge versus the data center, how algorithms are being compressed, and what techniques are being used to speed up these chips and reduce power. https://youtu.be/d32jtdFwpcE    ... » read more

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