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FPGA Architecture for Deep Learning: Survey and Future Directions

arxiv.org

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Re: FPGA Architecture for Deep Learning: Survey and Future Directions

#131

Post by novaRom »

zachbee wrote: post #130
sitkack wrote: post #98

Earlier quoted context omitted.

Are you trying to scare people away from FPGAs? GPUs aren't actually that _good_ at deep learning, but they are in the right place at the right time. You can rent high end FPGAs on AWS, https://github.com/aws/aws-fpga there is no better time to get into FPGAs. On the low end there is the excellent https://hackaday.com/2019/01/14/ulx3s-an-open-source-lattice... Modern FPGA platforms like Xilinx Alveo have 35TB/s of SR…

I'm definitely not! I'm a hardware designer and I work with FPGAs all the time, for both work and for personal projects. Like with all things, there's a right tool for every job, and I think for modern DL algorithms like Transformers, GPUs and AI ASICs are the better tools. For rapid hard prototyping, or for implementing specialized architectures, FPGAs are far better.

Large fast FPGAs are great but very expensive, small size slow FPGAs are not practical for most solutions, where ARM controllers are used, significantly cheaper.

Re: FPGA Architecture for Deep Learning: Survey and Future Directions

#132

Post by sitkack »

novaRom wrote: post #131
zachbee wrote: post #130

Earlier quoted context omitted.

I'm definitely not! I'm a hardware designer and I work with FPGAs all the time, for both work and for personal projects. Like with all things, there's a right tool for every job, and I think for modern DL algorithms like Transformers, GPUs and AI ASICs are the better tools. For rapid hard prototyping, or for implementing specialized architectures, FPGAs are far better.

Large fast FPGAs are great but very expensive, small size slow FPGAs are not practical for most solutions, where ARM controllers are used, significantly cheaper.

Cost and practicality are context dependent.

Re: FPGA Architecture for Deep Learning: Survey and Future Directions

#133

Post by jacobgorm »

zachbee wrote: post #92

The big challenge when it comes to using FPGAs for deep learning is pretty simple: all of that reprogrammability comes at a performance cost. If you're doing something highly specific that conventional GPUs are bad at, like genomics research [1] or high-frequency trading [2], the performance tradeoff is worth it. But for deep learning, GPUs and AI ASICs are highly optimized for most of these computations, and an FPGA…

I was part of a startup that did ternary CNNs on FPGA in 2017. It involved a ton of nitty gritty work and massive loss of generalit, and in the end a Raspberry Pi could solve the same problem faster and cheaper.

Re: FPGA Architecture for Deep Learning: Survey and Future Directions

#134

Post by adultSwim »

zachbee wrote: post #92

The big challenge when it comes to using FPGAs for deep learning is pretty simple: all of that reprogrammability comes at a performance cost. If you're doing something highly specific that conventional GPUs are bad at, like genomics research [1] or high-frequency trading [2], the performance tradeoff is worth it. But for deep learning, GPUs and AI ASICs are highly optimized for most of these computations, and an FPGA…

What about FPGAs as a means to experiment in ML and hardware architectures?

Re: FPGA Architecture for Deep Learning: Survey and Future Directions

#135

Post by bob_theslob646 »

shrubble wrote: post #104
mikewarot wrote: post #96

My pet project is to take these ideas and go to the logical end, arriving at a systolic array I call a BitGrid. It's a Cartesian grid of 4 bit look up tables, with bits to/from each neighbor. This allows each output to be independent, maximizing utilization. To solve timing issues, each cell would be clocked, with 2 phases for the grid, in a checkerboard pattern. This makes all inputs stable, and timing deterministic…

DE Shaw has a systolic supercomputer: https://en.m.wikipedia.org/wiki/Anton_(computer)

How do you know of this? This is really cool
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