Improving Algorithms With High-Level Synthesis

By Ann Mutschler - 30 Dec, 2019 - Comments: 0

Most computer algorithms today are developed in high-level languages on general-purpose computers. But someday they may be deployed in embedded systems where the development, verification, and validation of algorithms is done in languages like python, Java, C++, or even numerical frameworks like MatLab. This is the goal of high-level synthesis (HLS), and it aims to solve a fundamental proble... » read more

Machine Learning Drives High-Level Synthesis Boom

By Kevin Fogarty - 06 Jun, 2019 - Comments: 0

High-level synthesis (HLS) is experiencing a new wave of popularity, driven by its ability to handle machine-learning matrices and iterative design efforts. The obvious advantage of HLS is the boost in productivity designers get from working in C, C++ and other high-level languages rather than RTL. The ability to design a layout that should work, and then easily modify it to test other confi... » read more

From AI Algorithm To Implementation

By Brian Bailey - 10 Apr, 2019 - Comments: 0

Semiconductor Engineering sat down to discuss the role that EDA has in automating artificial intelligence and machine learning with Doug Letcher, president and CEO of Metrics; Daniel Hansson, CEO of Verifyter; Harry Foster, chief scientist verification for Mentor, a Siemens Business; Larry Melling, product management director for Cadence; Manish Pandey, Synopsys fellow; and Raik Brinkmann, CEO ... » read more

The Automation Of AI

By Brian Bailey - 28 Mar, 2019 - Comments: 0

Edge Inferencing Challenges

By Ed Sperling - 10 Jan, 2019 - Comments: 0

Geoff Tate, CEO of Flex Logix, talks about balancing different variables to improve performance and reduce power at the lowest cost possible in order to do inferencing in edge devices. https://youtu.be/1BTxwew--5U » read more

Optimizing Machine Learning Workloads On Power-Efficient Devices

By Arm - 11 Apr, 2018 - Comments: 0

Software frameworks for neural networks, such as TensorFlow, PyTorch, and Caffe, have made it easier to use machine learning as an everyday feature, but it can be difficult to run these frameworks in an embedded environment. Limited budgets for power, memory, and computation can all make this more difficult. At Arm, we’ve developed Arm NN, an inference engine that makes it easier to target di... » read more

Bridging Machine Learning’s Divide

By Brian Bailey - 08 Feb, 2018 - Comments: 0

There is a growing divide between those researching [getkc id="305" comment="machine learning"] (ML) in the cloud and those trying to perform inferencing using limited resources and power budgets. Researchers are using the most cost-effective hardware available to them, which happens to be GPUs filled with floating point arithmetic units. But this is an untenable solution for embedded infere... » read more

Verifying AI, Machine Learning

By Ed Sperling - 28 Sep, 2017 - Comments: 0

[getperson id="11306" comment="Raik Brinkmann"], president and CEO of [getentity id="22395" e_name="OneSpin Solutions"], sat down to talk about artificial intelligence, machine learning, and neuromorphic chips. What follows are excerpts of that conversation. SE: What's changing in [getkc id="305" kc_name="machine learning"]? Brinkmann: There’s a real push toward computing at the edge. ... » read more

Speeding Up Neural Networks

By Ed Sperling - 27 Apr, 2017 - Comments: 2

Neural networking is gaining traction as the best way of collecting and moving critical data from the physical world and processing it in the digital world. Now the question is how to speed up this whole process. But it isn't a straightforward engineering challenge. Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. Th... » read more

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tag: Caffe

Improving Algorithms With High-Level Synthesis

Machine Learning Drives High-Level Synthesis Boom

From AI Algorithm To Implementation

The Automation Of AI

Edge Inferencing Challenges

Optimizing Machine Learning Workloads On Power-Efficient Devices

Bridging Machine Learning’s Divide

Verifying AI, Machine Learning

Speeding Up Neural Networks

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