InsideDSP — In-depth analysis and opinion

BrainChip Leverages Software, Acceleration Hardware to Jumpstart Emerging Neural Network Approach

Convolutional neural networks (CNNs) may be the hot artificial intelligence (AI) technology of the moment, in no small part due to their compatibility for both training and inference functions with existing GPUs, FPGAs and DSPs as accelerators, but they're not the only game in town. Witness, for example, Australia-based startup BrainChip Holdings and its alternative proprietary spiking neural network (SNN) technology (Figure 1). Now armed with both foundation software and acceleration hardware Read more...

Jeff Bier’s Impulse Response—Is Deep Learning the Solution to All Computer Vision Problems?

At the Embedded Vision Summit in May, I had the privilege of hearing a brilliant keynote presentation from Professor Jitendra Malik of UC Berkeley. Malik, whose research and teaching have helped shape the field of computer vision for 30 years, explained that he had been skeptical about the value of deep neural networks for computer vision, but ultimately changed his mind in the face of a growing body of impressive results. There’s no question that deep neural networks (DNNs) have transformed Read more...

Case Study: The Path to Credible, Convincing Benchmarks

Benchmarks are an important resource for system and processor designers alike. System designers need good benchmarks to understand how a processor will meet the requirements of their applications and to compare processors accurately. Processor designers need good benchmarks to assess how their processors will perform on their target applications and to prove their processors' capabilities to customers. But before determining what a good benchmark is, it’s best to ask what you want the Read more...

Next-gen Qualcomm Camera Modules, ISP Target Depth Sensing, Other Computer Vision Tasks

Qualcomm is expanding its reference camera module program with three new configurations targeting biometrics and depth-sensing functions in Android-based smartphones, tablets, AR (augmented reality) and VR (virtual reality) headsets, and other devices. While the modules' targeted computer vision tasks tend to be computationally intensive, a next-generation ISP (image signal processor) core optimized for the functions is intended to offload CPU, GPU and DSP resources inside a SoC, delivering Read more...

Intel-influenced Movidius Neural Compute Stick Increases Memory, Lowers Price, Reprioritizes Frameworks

When Movidius unveiled the Fathom Neural Compute Stick, based on its Myriad 2 VPU (vision processor), at the May 2016 Embedded Vision Summit, the company targeted a $99 price tag and initially planned to support the TensorFlow framework, with support for Caffe and other frameworks to follow. A lot's changed in a year-plus, most notably Intel's acquisition of Movidius announced in September. The company's new version of the Neural Compute Stick drops the price by 20%, switches from plastic to Read more...

Jeff Bier’s Impulse Response—Embedded Vision Enables Truly Smart Cities

On a recent vacation, I was struck by how indispensable smartphones have become for travelers. GPS-powered maps enable us to navigate unfamiliar cities. Language translation apps help us make sense of unfamiliar languages. Looking for a train, taxi, museum, restaurant, shop or park? A few taps of the screen and you’ve found it. And yet, there’s a vast amount of useful information that isn’t at our fingertips. Where’s the nearest available parking space? How crowded is that bus, restaurant, or Read more...

Case Study: Unlocking the Capabilities of Today’s Complex SoCs for Vision

As SoCs become more complex and specialized, incorporating numerous and varied processor cores and dedicated accelerators, it has become more and more difficult to program them. This is particularly true of chips targeting vision-based applications. To meet the performance demands and high data rates of vision applications, vendors are designing heterogeneous devices that incorporate different classes of processors—CPUs, DSPs, GPUs, FPGAs, and special-purpose engines. Programming each of these Read more...

AImotive Expands Into Silicon IP for Deep Learning Inference Acceleration

AImotive has been developing its aiDrive software suite for advanced driver assistance systems (ADAS) and autonomous vehicles for nearly a decade. As the computing demands of its algorithms continue to increase, the company is finding that conventional processor approaches aren't keeping pace. In response, and with an eye both on vehicle autonomy and other deep learning opportunities, the company began developing its own inference acceleration engine, aiWare, at the beginning of last year. An Read more...

Synopsys Broadens Neural Network Engine IP Core Family

Last June, when Synopsys unveiled its latest-generation DesignWare EV6x vision processor core, the company also introduced an 880-MAC, 12-bit convolutional neural network (CNN) companion processor, the CNN880. Although the CNN880 is optional for Synopsys customers using the EV6x, it's been a key factor (often the lead factor, in fact) in greater than 90% of EV6x customer engagements, according to Product Marketing Manager Gordon Cooper. And although a year ago, an 880-MAC architecture was at Read more...

Jeff Bier’s Impulse Response—Privacy in the Era of Ubiquitous Cameras and AI

Lately I’ve been thinking about the relationship between embedded vision and privacy. Surveillance cameras are nothing new, of course. For decades, they’ve been ubiquitous in and around restaurants, stores, banks, offices, airports, train stations, etc. In the course of a typical week, I’d guess that my image is captured by dozens of these cameras. As someone who values privacy, the presence of so many surveillance cameras can be unsettling. But I’ve been comforted by the idea of “privacy Read more...