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re: Opinion: Using GPUs to accelerate EDA applications
docdivakar   5/11/2012 6:09:10 AM
Existing (not optimally parallel) EDA tools can still exploit the operating system to benefit from parallelism. Examples abound, like the pattern-based DRC; in the TCAD area, computational lithography, etc. MP Divakar

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re: Opinion: Using GPUs to accelerate EDA applications
urital.rocketick   5/7/2012 5:31:01 AM
Hi TingLu, FPGA-based accelerators enable to run chip designs at MHz speeds and to debug system-level scenarios in the lab, but they are not simulators. It is just a different product category. Pros: - You can reach 1-10MHz speeds with them and therefore debug your driver and even your application in embedded systems Cons: - They are very expensive. - Require significant ramp-up time, and then if you change your code or libraries you are not really debugging your real silicon design - Does not work alongside your existing test-bench (verification environment), and if it does you cannot reach MHz speeds. - Limited in capacity (to scale you need to add more FPGAs/boxes but then you trade-off with speed) - Lack support for non-synthesize-able code - No support for 4-state logic - Lack full visibility - Long compilation time (require to synthesys and place-and-route)

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re: Opinion: Using GPUs to accelerate EDA applications
TingLu   5/7/2012 4:17:38 AM
Simulation acceleration has been dominant by FPGA. I am wondering how well it is compared with GPU.

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re: Opinion: Using GPUs to accelerate EDA applications
Les_Slater   5/6/2012 1:20:21 AM
The problem of rethinking algorithmic foundations is an interesting one. This needs to be taken on as a general formality of a geometry of problem space.

Karl Fergusen
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re: Opinion: Using GPUs to accelerate EDA applications
Karl Fergusen   5/3/2012 7:05:05 PM
We started using Jacket a few months ago to accelerate MatLab codes at L-3 on the GPU. Awesome speedups! Parts Search

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What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.
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