Processors

DEEPX Demonstration of Its Product Portfolio for Empowering Edge AI Solutions

Tim Park, director of strategic marketing for DEEPX, demonstrates the company’s latest edge AI and vision technologies and products at the 2023 Embedded Vision Summit. Specifically, Park demonstrates DEEPX’s comprehensive product portfolio. With a range of edge AI chips targeting various applications, from low power to high performance, DEEPX offers compelling solutions with a strong […]

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DEEPX Demonstration of Empowering Edge AI Technology with Flexibility, Accuracy and Power Efficiency

Jay Kim, EVP of Technology for DEEPX, demonstrates the company’s latest edge AI and vision technologies and products at the 2023 Embedded Vision Summit. Specifically, Kim demonstrates the key features embedded in DEEPX’s edge AI chip technology. Kim outlines its improved flexibility, accuracy, and power/performance efficiency, attributes that enable businesses to access vital data for

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DEEPX Demonstration of Simplifying Software Development with DEEPX’s Two-step SDK

Jay Kim, EVP of Technology for DEEPX, demonstrates the company’s latest edge AI and vision technologies and products at the 2023 Embedded Vision Summit. Specifically, Kim demonstrates the simplicity of using DEEPX’s software development kit (SDK). Kim shows how to choose a target application and select an AI software framework in just two easy steps.

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Reflections from RSS: Three Reasons DL Fails at Autonomy

This blog post was originally published by Opteran Technologies. It is reprinted here with the permission of Opteran Technologies. Last week I had the pleasure of attending, and presenting at, the annual Robotics: Science and Systems (RSS) in Daegu, South Korea.  RSS ranks amongst the most prestigious of the international robotics conferences, and brings together

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“How Transformers Are Changing the Nature of Deep Learning Models,” a Presentation from Synopsys

Tom Michiels, System Architect for ARC Processors at Synopsys, presents the “How Transformers Are Changing the Nature of Deep Learning Models” tutorial at the May 2023 Embedded Vision Summit. The neural network models used in embedded real-time applications are evolving quickly. Transformer networks are a deep learning approach that has become dominant for natural language

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Get a Clearer Picture of Vision Transformers’ Potential at the Edge

This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. Scenario: Corporate security staff get an alert that a video camera has detected a former employee entering an off-limits building. Scenario: A radiologist receives a flag that an MRI contains early markers for potentially abnormal tissue growth.

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“Making GANs Much Better, or If at First You Don’t Succeed, Try, Try a GAN,” a Presentation from Perceive

Steve Teig, CEO of Perceive, presents the “Making GANs Much Better, or If at First You Don’t Succeed, Try, Try a GAN” tutorial at the May 2023 Embedded Vision Summit. Generative adversarial networks, or GANs, are widely used to create amazing “fake” images and realistic, synthetic training data. And yet, despite their name, mainstream GANs

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BrainChip’s Latest US Patent Award Extends Intellectual Property Strength and its Leadership in Edge Learning

Laguna Hills, Calif. – July 19, 2023 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, was awarded its latest US patent, further strengthening the company’s neuromorphic technology portfolio and demonstrating the company’s competitive research strength. Patent No. US 11,704,549, “Event-Based

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A Buyers Guide to an NPU

This blog post was originally published at Expedera’s website. It is reprinted here with the permission of Expedera. Choosing the right inference NPU (Neural Processing Unit) is a critical decision for a chip architect. There’s a lot at stake because the AI landscape constantly changes, and the choices will impact overall product cost, performance, and

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Can Compute-in-memory Bring New Benefits To Artificial Intelligence Inference?

This blog post was originally published at Expedera’s website. It is reprinted here with the permission of Expedera. Compute-in-memory (CIM) is not necessarily an Artificial Intelligence (AI) solution; rather, it is a memory management solution. CIM could bring advantages to AI processing by speeding up the multiplication operation at the heart of AI model execution.

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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