FPGA for Deep Learning: Survey and Future Directions

• wokwi-verilog-gds-test

  20 55 10.0 Verilog

https://tinytapeout.com/ now lest you purchase additional tiles for $50, each tile supports about 1k digital logic gates.

Next one closes June 1.

https://tinytapeout.com/faq/

You might enjoy this talk from the last Latchup on Wave Pipelining

https://fossi-foundation.org/latch-up/2024#riding-the-wave-b...

https://www.cs.princeton.edu/courses/archive/fall01/cs597a/w...

• aws-fpga

  3 1,471 1.0 VHDL

  Official repository of the AWS EC2 FPGA Hardware and Software Development Kit

  

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 SRAM bandwidth and 460GB/s of HBM bandwidth. https://www.xilinx.com/products/boards-and-kits/alveo/u55c.h...

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• FPGA-radio

  1 66 - VHDL

  Software Defined Radio in FPGA uses LVDS IO pins as 1-bit ADC

I work at one of the big 3 FPGA companies, so I can give you an idea of where our teams spend most of their time, and you can translate that into a hobbyist project as you will.

1. Video and Broadcast. Lots of things to be done here. New protocols are being introduced every year by IEEE for sending video between systems. Most cutting-edge cameras have some sort of FPGA inside doing niche image processing. You can get a sensor and build yourself your own Camera-on-Chip. It's a fantastic way to lose a year or two (I can attest to that). Some good material on the matter here: https://www.mathworks.com/discovery/fpga-image-processing.ht...

2. Compute Acceleration. This is more data centre-specific. SmartNICs, IPUs and the like. Hard to make a dent unless you want to spend 200k on a DevKit, but you could prototype one on a small scale. Some sort of smart FPGA switch that redirects Ethernet traffic between a bunch of Raspberry Pis dependent on one factor or another. One company that comes to mind is Napatech. They make a bunch of really interesting FPGA servers systems: https://www.napatech.com/products/nt200a02-smartnic-capture/

3. Robotics and Computer Vision. Plenty of low-hanging fruit to be plucked here. A rediculous amount of IO, all needed to work in near realtime. Hardware acceleration kernels on top of open standards like ROS 2. I always point people in the direction of Acceleration Robotics' startup in Barcelona for this. They're epic: https://github.com/ros-acceleration

4. Telecomunications. This is a bit of a dark art area for me, where the RF engineers get involved. From what my colleagues tell me, FPGAs are good for this because any other device doesn't service the massive MIMO antenna arrays besides building custom ASICs, and the rate of innovation in this area means an ASIC made one year is redundant the next. Software-defined radios are the current trend. You could have fun making your own radio using an FPGA: https://github.com/dawsonjon/FPGA-radio

• t1

  1 86 9.8 Scala
  

This drops you into a shell, with, and you can start simulating with "ip-emulator --no-logging -C yourProgram", see the tests/ director for example code.

[0] https://github.com/chipsalliance/t1

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