tag: neural networks

Blog Review: Oct. 10
By Jesse Allen - 10 Oct, 2018 - Comments: 0

In a video, Cadence's Megha Daga dives into sparsity in neural networks and how it affects bandwidth, performance, and power efficiency. In a video, Mentor's Colin Walls takes a look at efficient embedded code, and why that means different things at different times. Synopsys' Eric Huang argues that in the realm of video standards, HDMI, DisplayPort, and USB Type-C are set to continue comp... » read more

Using ASICs For AI Inferencing
By Ed Sperling - 10 Oct, 2018 - Comments: 0

Flex Logix’s Cheng Wang looks at why ASICs are the best way to improve performance and optimize power and area for inferencing, and how to add flexibility into those designs to deal with constantly changing algorithms and data sets. https://youtu.be/XMHr7sz9JWQ » read more

AI Chips Must Get The Floating-Point Math Right
By Sergio Marchese - 27 Sep, 2018 - Comments: 0

Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks today are often based on operations that use multiplication and addition of floating-point values, which subsequently need to be scaled to different sizes and for different needs. Modern FPGAs such as Intel Arria-10 ... » read more

Week In Review: Design, Low Power
By Jesse Allen - 21 Sep, 2018 - Comments: 0

Tools & IP Cadence unveiled deep neural-network accelerator (DNA) AI processor IP, Tensilica DNA 100, targeted at on-device neural network inference applications. The processor is scalable from 0.5 TMAC (Tera multiply-accumulate) to 12 TMACs, or 100s of TMACs with multiple processors stacked, and the company claims it delivers up to 4.7X better performance and up to 2.3X more performance p... » read more

Is Your AI SoC Secure?
By Dana Neustadter - 13 Sep, 2018 - Comments: 0

As artificial intelligence (AI) enters every application, from IoT to automotive, it is bringing new waves of innovation and business models, along with the need for high-grade security. Hackers try to exploit vulnerabilities at all levels of the system, from the system-on-chip (SoC) up. Therefore, security needs to be integral in the AI process. The protection of AI systems, their data, and th... » read more

Intel’s Next Move
By Ed Sperling - 11 Sep, 2018 - Comments: 0

Gadi Singer, vice president and general manager of Intel's Artificial Intelligence Products Group, sat down with Semiconductor Engineering to talk about Intel's vision for deep learning and why the company is looking well beyond the x86 architecture and one-chip solutions. SE: What's changing on the processor side? Singer: The biggest change is the addition of deep learning and neural ne... » read more

Flexible, Energy-Efficient Neural Network Processing At 16nm
By Geoff Tate - 06 Sep, 2018 - Comments: 0

At Hot Chips 30, held in August in Silicon Valley, Harvard University (Paul Whatmough, SK Lee, S Xi, U Gupta, L Pentecost, M Donato, HC Hseuh, Professor Brooks and Professor Gu) made a presentation on “SMIV: A 16nm SoC with Efficient and Flexible DNN Acceleration for Intelligent IOT Devices. ” (Their complete presentation is available now on the Hot Chips website for attendees and will be p... » read more

Huge Performance Gains Ahead
By Ed Sperling - 30 Aug, 2018 - Comments: 0

Rambus Chief Scientist Craig Hampel talks about what will drive the next big performance gains after Moore’s Law, from the data center to the edge. https://youtu.be/ItHCsei7YTc » read more

Big Changes For Mainstream Chip Architectures
By Ed Sperling - 23 Aug, 2018 - Comments: 3

Chipmakers are working on new architectures that significantly increase the amount of data that can be processed per watt and per clock cycle, setting the stage for one of the biggest shifts in chip architectures in decades. All of the major chipmakers and systems vendors are changing direction, setting off an architectural race that includes everything from how data is read and written in m... » read more

System Bits: Aug. 21
By Ann Mutschler - 21 Aug, 2018 - Comments: 0

Two types of computers create faster, less energy-intensive image processor for autonomous cars, security cameras, medical devices Stanford University researchers reminded that the image recognition technology that underlies today’s autonomous cars and aerial drones depends on artificial intelligence. These are the computers that essentially teach themselves to recognize objects like a dog, ... » read more

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