BrianChip_logo

BrainChip Adds Thomas Stengel as Vice President of Americas Business Development

BrainChip, News /

Leadership Team in Place to Drive Sales of new AI-Based BrainChip Studio Video Analytic Solutions Highlights: Tom Stengel joins as VP of Business Development for the Americas as the Company launches BrainChip Studio, a commercially available integrated software suite for pattern and facial recognition analytics. BrainChip expands its sales organization with a 30-year industry veteran […]

BrainChip Adds Thomas Stengel as Vice President of Americas Business Development Read More +

npe-landing-page_0

Snapdragon Neural Processing Engine Now Available on Qualcomm Developer Network

News /

Designed to Fuel On-device Artificial Intelligence Through Developers and Manufacturers Jul 25, 2017 – San Diego – Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated (NASDAQ: QCOM), announced today the immediate availability of the Qualcomm® Snapdragon™ Neural Processing Engine (NPE) software development kit (SDK) on Qualcomm Developer Network. The Snapdragon NPE is the first deep

Snapdragon Neural Processing Engine Now Available on Qualcomm Developer Network Read More +

“What is an Embedded Vision Processor?,” a Video from Synopsys

Videos /

This video, one in a series published by Alliance member company Synopsys, explains what is included in embedded vision processors, the features and functions that they provide, and the tools designers use for implementation. The video also compares (from Synopsys' perspective) embedded vision processors to GPUs, DSPs, and FPGAs.

“What is an Embedded Vision Processor?,” a Video from Synopsys Read More +

JetPack_31_600

JetPack 3.1 Doubles Jetson’s Low-Latency Inference Performance

Uncategorized /

Today, NVIDIA released JetPack 3.1, the production Linux software release for Jetson TX1 and TX2. With upgrades to TensorRT 2.1 and cuDNN 6.0, JetPack 3.1 delivers up to a 2x increase in deep learning inference performance for real-time applications like vision-guided navigation and motion control, which benefit from accelerated batch size 1. The improved features

JetPack 3.1 Doubles Jetson’s Low-Latency Inference Performance Read More +

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies

Algorithms & Models, Imagination Technologies, Processors, Software, Tools, Videos /

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Training CNNs for Efficient Inference" tutorial at the May 2017 Embedded Vision Summit. Key challenges to the successful deployment of CNNs in embedded markets are in addressing the compute, bandwidth and power requirements. Typically, for mobile devices, the problem lies in the inference, since the

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies Read More +

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies

Algorithms & Models, Imagination Technologies, Processors, Software, Tools, Videos /

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Training CNNs for Efficient Inference" tutorial at the May 2017 Embedded Vision Summit. Key challenges to the successful deployment of CNNs in embedded markets are in addressing the compute, bandwidth and power requirements. Typically, for mobile devices, the problem lies in the inference, since the

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies Read More +

“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel

Algorithms & Models, Intel, Processors, Software, Tools, Videos /

Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit. Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In

“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel Read More +

“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel

Algorithms & Models, Intel, Processors, Software, Tools, Videos /

Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit. Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In

“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel Read More +

Quad_laptop_web_600

Intel Democratizes Deep Learning Application Development with Launch of Movidius Neural Compute Stick

Uncategorized /

Today, Intel launched the Movidius™ Neural Compute Stick, the world’s first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge. Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce

Intel Democratizes Deep Learning Application Development with Launch of Movidius Neural Compute Stick Read More +

TensorFlowLogo

Building Mobile Apps with TensorFlow: An Interview with Google’s Pete Warden

Articles, Edge AI and Vision Alliance, Technical Articles /

Pete Warden, Google Research Engineer and technical lead on the company's mobile/embedded TensorFlow team, is a long-time advocate of the Embedded Vision Alliance. Warden has delivered presentations at both the 2016 ("TensorFlow: Enabling Mobile and Embedded Machine Intelligence") and 2017 ("Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP") Embedded Vision Summits, along with

Building Mobile Apps with TensorFlow: An Interview with Google’s Pete Warden Read More +

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.

Pages

Topics

Contact

Address

Berkeley Design Technology, Inc.
PO Box #4446
Walnut Creek, CA 94596

Phone
Phone: +1 (925) 954-1411
Scroll to Top