Processors

Texas Instruments Sitara SoCs Integrate Diverse Processing Resources

It's becoming increasingly possible, thanks to APIs and languages such as Khronos' OpenCL, for applications to efficiently harness the heterogenous computing resources available in modern SoCs. And it's therefore increasingly common for SoC designers to include a variety of both general-purpose and function-specific parallel-processing cores on-chip, leveraging the prodigious transistor budgets available on modern lithography processes. These trends are fully evident with Texas Instruments' 28 Read more...

CEVA Software Framework Brings Deep Learning to Embedded Vision Systems

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 layers of computation nodes – have shown particularly impressive results on challenging problems that thwart traditional feature-based techniques; when attempting to identify non-uniform objects, for example, or Read more...

Jeff Bier’s Impulse Response—When Comparing Processors, Beware of the Uncertainty Principle

My colleagues and I at BDTI frequently evaluate processors – sometimes on behalf of processor suppliers, and sometimes on behalf of processors users. Comparing processors can be complicated, given their many attributes, including some (like energy efficiency) that are difficult to compare accurately, and others (like ease-of-use) that are inherently subjective. Several recent conversations with processor suppliers have reminded me of another serious hazard in processor comparisons, one that I Read more...

Case Study: Digital Signal Processing Library Development Enables Effective Processor Deployments

As applications become more complex, and processors become more powerful, system developers increasingly rely on off-the-shelf software components to enable rapid and efficient application development. This is particularly true in digital signal processing, where application developers expect to have access to libraries of optimized building-block functions to speed their work. A leading SoC developer recently contracted BDTI to assist it in developing a comprehensive library of software Read more...

Qualcomm's QDSP6 v6: Imaging and Vision Enhancements Via Vector Extensions

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 covered the v4 "Hexagon" DSP core in mid-2012 and early 2013, along with the v5 Hexagon architecture later that same year. Now, with its Hexagon v6 DSP core, which will see its first silicon implementation in Read more...

ARC Processor Core Enhancements Promise Performance, Energy Consumption Improvements

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 (“D” denoting DSP). This year's follow-on EM9D and EM11D make what at first glance seem to be minor upgrades, in the form of an optional incremental 2-64 KB of special-purpose embedded memory. But, according to company Read more...

Freescale Introduces New Embedded Processors and Modules

Back in early 2013, the smartphone market was red hot, as was demand for Amazon's Kindle and other e-book readers. And the tablet market, although comparatively nascent, was in a rapid growth phase, as was interest in alternative computer platforms such as Google's Chrome O/S-based products. The substantial processing demands of these and other similar applications are evident in the formidable resources integrated within Freescale Semiconductor's i.MX 6 family introduced that same year: one to Read more...

Sensory Adds Deep Learning Support to TrulyHandsFree

Sensory's TrulyHandsFree software, which InsideDSP last covered at its v3 introduction in early 2013, precedes limited-vocabulary speech recognition with voice detection involving a specific key word or phrase. And with latest version 4.0, Sensory adopts convolutional (i.e. "deep learning") neural network (CNN) techniques. Jeff Bier began a recent editorial with the following statement: Lately, neural network algorithms have been gaining prominence in computer vision and other fields where Read more...

NVIDIA Toolsets Target GPU Acceleration of Deep Learning, Other Algorithms for High-Performance Computing

"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. However, with the emergence of programmable shader-based architectures beginning with NVIDIA's mainstream GeForce 3 line in 2001 and joined by ATI Technologies' (now AMD's) Radeon 9700 and derivatives unveiled the following Read more...

Jeff Bier’s Impulse Response—Neural Network Processors: Has Their Time Come?

Lately, neural network algorithms have been gaining prominence in computer vision and other fields where there's a need to extract insights based on ambiguous data. As I wrote last year, classical computer vision algorithms typically attempt to identify objects by first detecting small features, then finding collections of these small features to identify larger features, and then reasoning about these larger features to deduce the presence and location of an object of interest (such as a face Read more...