processors

Next-generation Cadence Tensilica Fusion DSP Core Expands Capabilities, Aspirations

The general trend of recent times is toward developing DSPs that are specialized for particular "killer" applications, such as wireless or embedded vision. Many other applications also need DSPs, however, and Cadence's Tensilica Fusion DSP core family, which complements the company's more application-specific DSPs, is one example of an architecture that aspires to serve a broader range of general applications. When Cadence introduced the first Fusion family member (now referred to as the Fusion 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...

Case Study: How to Implement Deep Learning for Vision on Embedded Processors

We're hearing more and more about the effectiveness of deep learning for a growing range of applications. With the surge in the volume of data available for training, and reduction in the cost of computing, technologists have turned to deep neural networks for solutions to compute-intensive applications such as speech and image recognition, 3D object recognition, and natural language processing. Particularly where the virtually unlimited processing power and memory of cloud and enterprise Read more...

New Synopsys Processor Core Targets Traditional- and Deep Learning-based Embedded Vision

In early 2015, Synopsys' DesignWare EV5x processor core family achieved notable attention for its unique co-processor engine focused on CNNs (convolutional neural networks) for object recognition and other vision functions. The company's new EV6x processor core family includes an upgraded CNN engine that delivers substantial performance gains over its predecessor while – in a nod to customers preferring to leverage "classical" computer vision algorithms – decoupling it from the remainder of the Read more...

HSA Foundation Aims for Broader Adoption of Coherent Memory Standard for Heterogeneous Processors

Modern SoCs increasingly contain a variety of processing resources: one or more CPU cores and a GPU, often with a DSP, programmable logic, or one or multiple special-purpose co-processors for tasks such as computer vision. Properly harnessed, such heterogeneous processors often deliver impressive performance at low cost and low power consumption. But mapping applications onto heterogeneous processors is challenging. OpenCL, a specification standard language and runtime from the Khronos Group, Read more...

Tensilica Vision P6 Processor Core Adopts Deep Learning-Focused Enhancements

Just last October, Cadence announced the then-latest generation in its computer vision processor core roadmap, the Tensilica Vision P5. Only seven months later, the Vision P5 has been superseded by the Vision P6 (Figure 1). This rapid product development pace reflects the equally rapid expansion and evolution of embedded computer vision applications. According to Cadence’s Chief Technology Officer Chris Rowen and Director of Product Marketing Pulin Desai, the company's new vision core is the Read more...

Movidius' Fathom Enables Embedded Deep Learning

In late January of this year, Movidius and Google broadened their collaboration plans, which had begun with 2014's Project Tango prototype depth-sensing smartphone. As initially announced, the companies’ broader intention to "accelerate the adoption of deep learning within mobile devices" was somewhat vague. However, as of earlier this month, at least some of the details of the planned collaboration become clearer, thanks to the unveiling of Movidius' Fathom Software Framework and Neural Read more...

Deep Learning and Digital Signal Processing: A Conversation with Cadence's Chris Rowen

Convolutional neural networks (CNNs) and other "deep learning" techniques are finding increasing use in a variety of detection and recognition tasks:  identifying music clips and speech phrases, for example, and finding human faces and other objects in images and videos. As a result, we’ve been covering deep learning concepts and implementations regularly in InsideDSP columns and news articles. Chris Rowen, Chief Technology Officer of Cadence Design Systems' IP Group, will be speaking about the Read more...

Case Study: BDTI Expertise Enables Selection of the Best Processor for Vision Applications

With more and more products incorporating computer vision functionality, semiconductor vendors are increasingly designing specialized processors for vision applications. For product developers, this means that identifying 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, processors for vision vary greatly in their architectures, making them tough to compare. Second, there is no universal set of benchmarks Read more...

CEVA-X Processor Cores Tout DSP, Control Plane Balance for Wireless

CEVA's DSP cores have long been a fixture in many mobile phone cellular modems, where they handle the tranceiver's digital signal processing functions, working alongside function-specific hardware accelerators. However, the physical layer coordination between the transmit and receiver subsystems, along with overall modem control and other housekeeping tasks, has long been the bailiwick of a real-time CPU core from ARM, Imagination Technologies or another supplier. CEVA aspires to evolve this Read more...