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.

Your company’s core expertise lies in developing innovative digital signal processing algorithms, not in porting and optimizing those algorithms to any of the dozens of processors that your customers use. But optimized implementations are often critical to enable customers to utilize your algorithms within their processing performance and power budgets. Good compilers are definitely helpful, but they invariably leave significant performance on the table. Engaging the processor experts at BDTI will ensure that your software is thoroughly optimized.

The SHARC DSP family has long been a design staple of mid-range and high-end audio, industrial and other digital signal-processing intensive applications. With its two new series of products, Analog Devices delivers dual-core SHARC to the market for the first time. And the ADSP-SC58x devices also integrate an ARM processor core to tackle system control code functions (Figure 1).

Practical computer vision (i.e. "embedded vision") is rapidly becoming a mainstream reality. Numerous processor chip and core suppliers have responded to increasing market demand with a variety of processor options. One of the first companies to target the vision processor space, Quebec, Canada-based CogniVue, has just unveiled its third-generation core architecture.

The semiconductor industry has been on a head-spinning merger binge lately. NXP is acquiring Freescale. Avago is acquiring Broadcom. Intel is acquiring Altera.

BDTI is well known for its software-related capabilities: performance- and power consumption-related benchmarking, for example, along with algorithm evaluation and development and optimization work. In such projects, BDTI frequently employs semiconductor manufacturers' evaluation boards and associated software toolsets, which are often combined to create development kits.

The "Internet of Things" (IoT), one of the hottest topics in technology today, is widely anticipated to be a notable driver of both semiconductor and software demand in coming years. Key to an understanding of the IoT opportunity, as a recent article published on the Embedded Vision Alliance website notes, is its machine-to-machine aspect.

These days we all have smart phones. Smart watches recently received a big boost with Apple's entry. And there is much talk of smart cities, smart factories, smart homes, smart trash receptacles...there's even a smart pacifier for your baby. I'm starting to feel like the term "smart" is so overused, it's becoming meaningless.

Semiconductor memory is increasing in capacity and becoming more cost-effective all the time. Yet, plenty of deeply embedded applications still exist for which every spare byte of RAM or flash memory is a precious commodity, especially those leveraging on-SoC storage versus discrete components. Tack on a performance-constrained DSP, intentionally speed-hampered to minimize power consumption, and a limited-capacity battery coupled with a multi-day or -week operating life expectation, and you've got a particularly challenging design on your hands.

The computer vision market is in a period of dramatic expansion. Market forecasts consolidated by Synopsys attest to the burgeoning adoption of practical computer vision (i.e. "embedded vision") technology (Figure 1) in a range of high-volume products. This growth is fueled by the increasing performance and decreasing cost and power consumption of processors, and by the growing awareness of the value that can be delivered via object detection, tracking, recognition and other vision processing functions.