Semiconductor innovations in computer vision and mobile photography

Michael McDonald, Skylane Technology

September 03, 2014

Michael McDonald, Skylane TechnologySeptember 03, 2014

The previous article in this series addressed the rapid growth in photography and computer vision (the ability to extract information from an image) and examined how computational solutions are addressing limitations in emerging camera sensors and optics. In addition to creating better photographs under increasingly difficult conditions, these same solutions are also enabling new user interfaces and experiences, creating amazing pictures that were previously impossible, and extracting information from the images to enable better management of the many photographs we are taking.

These promising new features are being added despite some major challenges. The sensor pixel size is rapidly approaching the wavelength of light, leaving limited opportunity to reduce costs by further shrinking pixels, the fundamental building block of the image sensor. In addition, the increasing performance requirements of video and vision provide challenges for mobile phones and embedded solutions that are also being called upon to run more and more applications. This article looks at some of the emerging silicon architectures in the form of optimized and innovative processors and sensors that are enabling these advanced features.

Quantifying the challenge
Computer vision is a rich source for extracting information. At the same time, it is also complex to extract relevant information from that medium. Some neuroscientists have estimated that the human brain uses over 60% of its capacity for vision processing. There are numerous elements that contribute to vision's complexity, including the volume of data, the image pre-processing and cleanup of that data, image analysis, and resulting decision-making.

The sheer amount of data acquired in each still image and video stream is significant. Within the next two years, the outward facing cell phone camera will collect on average over 12 million pixels per image, according to industry experts (Figure 1). Many of these cameras also capture video at a rate of 15 frames per second at 12 million pixels per frame, or 30 frames per second at high definition resolutions (approximately 2.1 million pixels per frame), meaning that these cameras are generating nearly 200 million pixels per second.

Figure 1: Nearly half of all cell phones manufactured in 2014 are forecasted to have camera sensors that are at least 8 Mpixels in resolution (courtesy OmniVision Technologies).

Emerging mobile phones will soon have the ability to capture 4k UHD (ultra high definition) video, which is over 8 million pixels per frame at 30 frames – or more! – per second or at least 240 million pixels per second. Interest in higher frame rates to capture rapid movements and generate slow-motion video clips will almost certainly increase this data rate even further.

As these pixel values are collected, a fixed-pipeline ISP (image signal processor), either located within the image sensor or alongside it, performs image processing that is largely tied to resolving image quality issues related to the sensor and lens. A Bayer filter-pattern sensor's output data, for example, is interpolated in order to generate a full RGB data set for each pixel. The ISP also handles initial color and brightness adjustments (fine-tuned for each image sensor device), noise removal, and focus adjustment.

Once basic image processing is complete, vision processing algorithms then extract data from the image in order to perform functions such as computational photography, object tracking, facial recognition, depth processing, and augmented reality. As the functional requirements vary widely, so too will their algorithms and resulting computational load. Even a relatively simple algorithm could involve over a hundred calculations per pixel; at nearly 200 million pixels coming through the system each second, that is over 20 billion operations per second.

While powerful desktop and notebook processors operating at multi-gigahertz speeds can shoulder such a computing load, mobile phone processors are challenged to meet these vision performance requirements due to their slower clock speeds. In addition, many processor architectures frequently shuttle data in and out of cache and external memory; at the data rates required of video, this interface is a performance bottleneck and consumes significant power. In order to address these limitations, mobile processor architectures are taking advantage of acceleration engines and adding new engines specifically optimized for vision. At the same time, algorithm developers are optimizing and re-writing algorithms to run on these new engines.

New vision processing architectures
New vision acceleration engines are coming in the form of GPUs, DSPs, and specialized vision processors that are capable of significantly higher levels of parallel processing. These are often SIMD (single instruction multiple data) architectures that leverage the fact that many vision algorithms perform the same functions on groups of pixels.

Rather than performing the function serially on each pixel, such architectures process them in parallel, which reduces clock speed and dynamic power consumption (Figure 2). Additionally, these architectures are fine-tuned to minimize external memory accesses, enabling them to alleviate this performance bottleneck, achieve lower power consumption, and potentially lower the price of the chip package via reduced memory bus size requirements.

Figure 2: In an SIMD architecture, a single instruction is capable of working on multiple pieces of data in parallel, while a typical CPU handles data operations serially.

The increasing importance of the GPU core is reflected in its increasing percentage of the overall silicon area of a mobile application processor. Chipworks’ examination of Apple’s latest "A7" SoC, for example, reveals that a larger portion of the chip is dedicated to the GPU than to the CPU (Figure 3).

Figure 3: The GPU core and associated logic consume more silicon space than the CPU core in Apple's A7 SoC.

Similarly, in TechInsights' examination of Samsung’s Exynos Octa mobile processor, the GPU core was larger than the combination of the quad core ARM Cortex-A15 CPU and its surrounding L2 cache memory (Figure 4). While GPUs arguably exist to support the robust gaming and other graphics capabilities of today's mobile devices, these same cores are emerging as powerful engines for computational photography and other vision applications.

Figure 4: The GPU, ISP, camera and video logic take up nearly as much area as the ARM Cortex-A7 and Cortex-A15 CPUs and associated cache in Samsung’s Exynos Octa application processor.

As these applications become increasingly commonplace, optimized silicon blocks are increasingly emerging. These vision-optimized architectures have many efficient small processors that enable them to dissect an image and then process each of the resulting blocks in parallel. The companies creating these architectures also recognize the performance bottlenecks and increased power consumption that come with moving images in and out of memory and have developed approaches that eliminate unnecessary data movement.

NVIDIA, for example, introduced a computational photography acceleration engine called Chimera in the Tegra 4 SoC. Apple's advertising similarly claims that the A7 SoC contains a "new image sensor processor" enabling faster image capture, focus, and video frame rates.

Qualcomm, aggressively pushing camera features such as HDR, face detection, augmented reality and other vision capabilities in the company's Snapdragon processors, integrates a "Hexagon" DSP core to offload some vision processing functions from the CPU and ISP.

And the startup Movidius provides the vision and imaging coprocessor chip used in Google’s Project Tango as well as some other vision consumer products in development.

Companies whose business model involves licensing IP processor cores also recognize the parallel processing needs of these burgeoning vision applications and are responding. Many of ARM's CPU cores include the NEON SIMD processing "engine", which is often used for vision processing.

Canadian company CogniVue provides licensable silicon IP core vision processor products for the automotive safety space. Core providers such as Apical, Tensilica (now part of Cadence), Tensilica), CEVA, Imagination Technologies, and videantis all now offer optimized cores for embedded vision that enable heavy vision processing while still fitting within the tight power budgets demanded by mobile system designs.

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