Gordon Cooper

  (all posts)
Gordon Cooper is a product marketing manager for Synopsys’ Embedded Vision Processor family. Cooper brings more than 20 years of experience in digital design, field applications and marketing at Raytheon, Analog Devices, and NXP to the role. Cooper also served as a Commanding Officer in the US Army Reserve, including a tour in Kosovo. He holds a Bachelor of Science degree in Electrical Engineering from Clarkson University.

Author's Latest Posts

How To Successfully Deploy GenAI On Edge Devices
By Gordon Cooper - 16 May, 2024 - Comments: 0

Generative AI (GenAI) burst onto the scene and into the public’s imagination with the launch of ChatGPT in late 2022. Users were amazed at the natural language processing chatbot’s ability to turn a short text prompt into coherent humanlike text including essays, language translations, and code examples. Technology companies – impressed with ChatGPT’s abilities – have started looking ... » read more

Virtualization: A Must-Have For Embedded AI In Automotive SoCs
By Gordon Cooper - 03 Aug, 2023 - Comments: 0

Virtualization, the process of abstracting physical hardware by creating multiple virtual machines (VMs) with independent operating systems and tasks, has been in computing since the 1960s. Now, with the need to optimize the utilization of large AI and DSP blocks in automotive SoCs, along with the need for increased functional safety in autonomous driving, virtualization is coming to power- an... » read more

Achieving Greater Accuracy In Real-Time Vision Processing With Transformers
By Gordon Cooper - 10 Jan, 2023 - Comments: 0

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications and got interesting results. While previously, vision tasks had been dominated by convolutional neural networks (CNNs), transformers have proven surprisingly adaptable to vision tasks like image cl... » read more

New Neural Processors Address Emerging Neural Networks
By Gordon Cooper - 05 May, 2022 - Comments: 0

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale Visual Recognition Competition (ILSVRC). AlexNet, and its successors, provided significant improvements in object classification accuracy at the cost of intense computational complexity and large da... » read more

New Vision Technologies For Real-World Applications
By Gordon Cooper - 03 Oct, 2019 - Comments: 0

Computer vision – the ability of a machine to ‘infer’ or extract useful information from a two-dimensional image or an uncompressed video stream of images – has the ability to change our lives. It can enable self-driving cars, empower robots or drones to see their way to delivering packages to your doorstep, and can turn your face into a payment method (Figure 1). To achieve these advan... » read more

Combining SLAM And CNN For High-Performance Augmented Reality
By Gordon Cooper - 04 Apr, 2019 - Comments: 0

Robotics and headsets or goggles are the most common hardware devices requiring AR/VR/mixed reality, and AR is coming to mobile phones, tablets, and automobiles as well. For hardware devices to see the world around them and add to that reality with inserted graphics or images, they need to determine their position in space and map the surrounding environment. Simultaneous localization and ma... » read more

Developing ASIL Ready SoCs For Self-Driving Cars
By Gordon Cooper - 10 May, 2018 - Comments: 0

Artificial intelligence (AI) and deep learning using neural networks is a powerful technique for enabling advanced driver-assistance systems (ADAS) and greater autonomy in vehicles. As AI research moves rapidly, designers are facing tough competition to provide efficient, flexible, and scalable silicon and software to handle deep learning automotive applications like inferencing in embedded vis... » read more

Low-Power Deep Learning Implementation For Automotive ICs
By Gordon Cooper - 01 Mar, 2018 - Comments: 0

Examples of automotive applications abound where high-performance, low-power embedded vision processors are used, from in-car driver drowsiness detection, to a self-driving car ‘seeing’ the road ahead with pedestrians, oncoming cars, or the occasional animal crossing the road. Implementing deep learning in these types of applications requires a lot of processing power with the lowest possib... » read more

Software Framework Requirements For Embedded Vision
By Gordon Cooper - 09 Nov, 2017 - Comments: 0

Deep learning techniques such as convolutional neural networks (CNN) have significantly increased the accuracy—and therefore the adoption rate—of embedded vision for embedded systems. Starting with AlexNet’s win in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), deep learning has changed the market by drastically reducing the error rates for image classification and d... » read more

The Evolution Of Deep Learning For ADAS Applications
By Gordon Cooper - 08 Jun, 2017 - Comments: 0

Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly. To accomplish this, embedded vision processors must be hardware optimized for performanc... » read more

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