Tools

Case Study: From Platform Selection to Optimized Application: Vision System Design Success

As computer vision is deployed into a variety of new applications, driven by the emergence of powerful, low-cost, and energy-efficient processors, companies need to find ways to squeeze demanding vision processing algorithms into size-, weight-, power, and cost-constrained systems. Fortunately for its clients, BDTI's skill in benchmarking has armed it with unique skill in optimizing software to best exploit processor capabilities. It's also provided BDTI with an in-depth understanding of the Read more...

Microchip 32-bit Microcontrollers Gain High-Precision Floating Point Capabilities

The decreasing cost-per-transistor delivered by modern semiconductor processes means that a number of previously rare embedded processor options are now increasingly common. This trend includes floating-point coprocessors, which are especially useful when migrating code originally developed on a PC to an embedded system. Recall that floating-point support on the PC wasn't a "given" until the first 1993-era Pentium processor-based systems hit the scene; earlier i486 CPUs offered the integrated Read more...

Jeff Bier’s Impulse Response—Why Do Embedded Processor Software Development Tools Suck? It’s the Unknown Knowns

In 2002, in a famous piece of unintentional rhetorical artistry, U.S. Defense Secretary Donald Rumsfeld spoke to reporters about "known knowns" (things you're aware that you know), "known unknowns" (things you're aware that you don't know) and "unknown unknowns" (things you're unaware that you don't know). As every programmer immediately realized, the category that Rumsfeld didn't mention is the "unknown knowns" – things you know but don't realize that you know. Lately, I've come to the view Read more...

Case Study: BDTI's Expert, Independent Analysis Enables Optimum Vision Processor Selection

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 challenging, for several reasons. First, these processors use very diverse architecture approaches, which makes it tough to compare them. Second, because vision applications and algorithms are also quite Read more...

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...

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...

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...