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Choose the Right ASIC Manufacturing Model for Your Business

REPLAY: GPU-Powered SPICE: The Way Forward for Analog Simulation

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REPLAY: eFPGA – "What a great idea! But I have no idea how I’d use it!"

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October 30, 2019October 30, 2019

Efficiency – Flex Logix’s Update on InferX™ X1 Edge Inference Co-Processor

Efficiency – Flex Logix’s Update on InferX™ X1 Edge Inference Co-Processor
by Randy Smith on 10-30-2019 at 10:00 am
Categories: eFPGA, Events, Flex Logix

Last week I attended the Linley Fall Processor Conference held in Santa Clara, CA. This blog is the first of three blogs I will be writing based on things I saw and heard at the event.

In April, Flex Logix announced its InferX X1 edge inference co-processor. At that time, Flex Logix announced that the IP would be available and that a chip,… Read More

September 30, 2019October 3, 2019

AI Hardware Summit, Report #2: Lowering Power at the Edge with HLS

AI Hardware Summit, Report #2: Lowering Power at the Edge with HLS
by Randy Smith on 09-30-2019 at 10:00 am
Categories: EDA, Events, Mentor, a Siemens Business

I previously wrote a blog about a session from Day 1 of the AI Hardware Summit at the Computer History Museum in Mountain View, CA, held just last week. From Day 2, I want to delve into this presentation by Bryan Bowyer, Director of Engineering, Digital Design & Implementation Solutions Division at Mentor, a Siemens Business.… Read More

July 18, 2019July 17, 2019

Semicon West 2019 – Day 2

Semicon West 2019 – Day 2
by Scotten Jones on 07-18-2019 at 10:00 am
Categories: AI, FD-SOI, FinFET, IC Knowledge, Semiconductor Services

Tuesday July 9^th was the first day the show floor was open at Semicon. The following is a summary of some announcements I attended and general observations.

AMAT Announcement

My day started with an Applied Materials (AMAT) briefing for press and analysts where they announced “the most sophisticated system they have ever released�… Read More

October 24, 2017

DSP-Based Neural Nets

DSP-Based Neural Nets
by Bernard Murphy on 10-24-2017 at 7:00 am
Categories: AI, CEVA, IoT, Mobile, Security
4 Comments

You may be under the impression that anything to do with neural nets necessarily runs on a GPU. After all, NVIDIA dominates a lot of what we hear in this area, and rightly so. In neural net training, their solutions are well established. However, GPUs tend to consume a lot of power and are not necessarily optimal in inference performance… Read More

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Tag: ai at the edge

Efficiency – Flex Logix’s Update on InferX™ X1 Edge Inference Co-Processor

AI Hardware Summit, Report #2: Lowering Power at the Edge with HLS

Semicon West 2019 – Day 2

DSP-Based Neural Nets

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