Software Development

Microsoft’s Deep Learning Toolkit Advances

Microsoft's Cognitive Toolkit (formerly CNTK) is a relatively recent entrant to the open-source deep learning framework market. And, as the company's Principal Researcher Cha Zhang acknowledged in a recent briefing, it has a ways to go before it can catch up with the population of developers enjoyed by well-known alternatives such as Caffe and Google's TensorFlow. Last year's transition from Microsoft's own CodePlex open source hosting site to the more widely known GitHub repository, along with Read more...

Cadence Doubles Down on Deep Learning

At last year's Embedded Vision Summit, Cadence unveiled the Tensilica Vision P6 DSP, which augmented the imaging and vision processing capabilities of its predecessors with the ability to efficiently execute deep neural network (DNN) inference functions. Cadence returned to the Summit this year with a new IP offering, the Vision C5 DSP core, focused exclusively on deep neural networks. Vision C5 is intended for use alongside another core, such as the Vision P6, which will handle image signal Read more...

NVIDIA's Jetson TX2: Embedded Designs Gain a Deep Learning Upgrade

Jetson TX2 is NVIDIA's latest board-level product targeted at computer vision, deep learning, and other embedded AI tasks, particularly focused on "at the edge" inference (when a neural network analyzes new data it’s presented with, based on its previous training) (Figure 1). It acts as an upgrade to both the Tegra K1 SoC-based Jetson TK1, covered in InsideDSP in the spring of 2014, and the successor Tegra X1-based Jetson TX1, which BDTI evaluated for deep learning and other computer vision Read more...

Jeff Bier’s Impulse Response—Will Deep Learning Displace All Other Computer Vision Techniques?

It's remarkable to see the range of applications in which deep neural networks are proving effective – often, significantly more effective than previously known techniques. From speech recognition to ranking web search results to object recognition, each day brings a new product or published paper with a new challenge tamed by deep learning. Computer vision, of course, is a field with significant deep learning activity. Deep learning is particularly appealing for visual perception because Read more...

Case Study: Deep Understanding of Processor Architectures and Computer Vision Algorithms is Key to a Breakthrough Product

Computer vision promises to be the key to the next set of "killer apps"—computer vision-enabled apps that will leverage artificial intelligence to help keep us safer and healthier. But computer vision algorithms are compute- and power-intensive, and need to process large amounts of data. These barriers have limited their use to enterprise, line-powered devices and cloud-assisted mobile devices. The implementation of computer vision algorithms on mobile processors, where compute resources are Read more...

New AMD Software Library, Hardware Support Deep Learning Acceleration

HPC (high-performance computing) servers, which have notably embraced the GPGPU (general-purpose computing on graphics processing units) concept in recent years, are increasingly being employed for computer vision and other deep learning-based applications. Beginning in late 2014, NVIDIA supplemented its general-purpose CUDA toolset for GPU-accelerated heterogeneous computing with its proprietary CuDNN software library, which codifies the basic mathematical and data operations at the core of Read more...

AMD's ROCm: CUDA Gets Some Competition

NVIDIA was an early and aggressive advocate of leveraging graphics processors for other massively parallel processing tasks (often referred to as general-purpose computing on graphics processing units, or GPGPU). The company's CUDA software toolset for GPU computing has to date secured only modest success in mobile and desktop PCs; with game physics processing acceleration, for example, along with still and video image processing acceleration. However, GPGPU has been embraced in the HPC (high- Read more...

Case Study: Balancing the Demands of Algorithms and the Capabilities of Processors When Designing Computer Vision Systems

There is now little question that deep learning is an effective means for a wide range of detection and recognition applications. It is increasingly used in computer vision, where it has vastly improved accuracy rates for object recognition. In some cases, the effectiveness of deep learning has also resulted in unrealistic—or, as-yet unrealizable—expectations. For example, one customer came to BDTI with a long list of detection and recognition functions that it wanted implemented on a low-cost Read more...

The CEVA-XM6 Vision Processor Core Boosts Performance for Embedded Deep Learning Applications

Hard on the heels of the public release of CEVA's second-generation convolutional neural network toolset, CDNN2, the company is putting the final touches on its fifth-generation processor core, the CEVA-XM6, designed to run software generated by that toolset. Liran Bar, the company's Director of Product Marketing, acknowledged in a recent briefing that the new core represents an evolutionary step, versus revolutionary break, from its predecessors: the CEVA-MM3101 (introduced in 2012) and the Read more...

CEVA Second-generation Deep Learning Toolset Supports Additional Frameworks and Topologies

Last year, when CEVA introduced the initial iteration of its CDNN (CEVA Deep Neural Network) toolset, company officials expressed an aspiration for CDNN to eventually support multiple popular deep learning frameworks. At the time, however, CDNN launched with support only for the well-known Caffe framework, and only for a subset of possible layers and topologies based on it. The recently released second-generation CDNN2 makes notable advancements in all of these areas, including both more fully Read more...