- NVIDIA's Jetson TX2: Embedded Designs Gain a Deep Learning Upgrade
- CEVA's XC12 DSP Core Targets Multi-Gbit Cellular, Wi-Fi Opportunities
- Jeff Bier’s Impulse Response—Will Deep Learning Displace All Other Computer Vision Techniques?
- Case Study: Hands-On Experience Delivers Insights Into Technologies and Trends for Deep Learning
- Himax, CEVA, emza Partner to Develop Low Power Vision Processing Platform
March 13, 2017 | Write the first comment.
Jetson TX2 is NVIDIA's latest board-level product targeted at computer vision, deep learning, and other embedded AI tasks, particularly focused on "at the edge" inference (when a neural network analyzes new data it’s presented with, based on its previous training) ( Figure 1
Processor vendors and system designers share a common concern: how to make sure their products meet customer needs. For processor vendors, a key challenge is to design architectures with enough performance to meet the demands of current and anticipated applications while staying within acceptable
As embedded processors and applications become increasingly complex, good benchmarks are more important than ever. System designers need good benchmarks to judge whether a processor will meet the needs of their applications, and to make accurate comparisons among processors. Processor developers
November 16, 2015 | Write the first comment.
A growing number of products are incorporating computer vision capabilities. This, in turn, has led to rapid growth in the number of processors being offered for vision applications. Selecting the best processor (whether a chip for use in a system design, or an IP core for use in an SoC) is
October 21, 2015 | Write the first comment.
As Jeff Bier has mentioned in several of his recent columns, deep learning algorithms have gained prominence in computer vision and other fields where there's a need to extract insights from ambiguous data. Convolutional neural networks (CNNs) – massively parallel algorithms made up of
Qualcomm has been evolving its in-house DSP core for many years. Originally developed for use in Qualcomm’s cellular modems, more recently it has also found use as an application co-processor, offloading multimedia tasks from the CPU in smart phones and tablets. Earlier InsideDSP articles
In May 2014 , Synopsys expanded its ARC EM licensable processor core product line, which BDTI described as historically being "vanilla" Harvard architecture CPUs with no DSP-optimized features, via the addition of the digital signal processing pipeline-equipped EM5D and EM7D
September 29, 2015 | Write the first comment.
Today's smartphones are technological marvels that deliver an extraordinary range of capabilities from GPS-based navigation to sophisticated photography. But sometimes we just want to make a phone call. And particularly when we're on the move, who hasn't struggled to hear the other
NVIDIA Toolsets Target GPU Acceleration of Deep Learning, Other Algorithms for High-Performance Computing
July 22, 2015 | Write the first comment.
"General-purpose GPU" (or "GPGPU") refers to the use of graphics processors for a variety of non-graphics tasks, and is a frequently discussed topic here at InsideDSP . GPUs are massively parallel processors, originally designed to only handle vertex and pixel operations.
With the MM3101 , launched at the January 2012 Consumer Electronics Show, silicon IP supplier CEVA for the first time provided a processor core with instructions and other features specifically tailored for computer vision algorithms. (The precursor MM2000 and MM3000 were focused