This is an FPGA accelerated solution for Logistic Regression BGD algorithm. It can provide up to 70x speedup compared to a single threaded execution and up to 12x compared to an 8 threaded Intel Xeon CPU execution respectively.
| Classes | Features |
|---|---|
| up to 64 | up to 2047 |
| Board |
|---|
| Xilinx Alveo U200 |
| Xilinx Alveo U250 |
| AWS VU9P (F1 instances) |
| Alibaba VU9P (F3 instances) |
| Any other Xilinx platform with at least the same amount of VU9P resources |
- The application code is located in the hosts_srcs directory. Accelerator kernel files are located under the kernel_srcs directory while any accelerator binaries will be compiled to the current directory.
- The Makefile will help you generate any host executable and accelerator .xclbin files.
A listing of all the files in this repository is shown below:
- Makefile
- hosts_srcs/
- LogisticRegression.cpp
- common/
- INcl.cpp (OpenCL wrapper functions)
- INcl.h
- inaccel/
- runtime-api.cpp (InAccel runtime abstraction layer)
- runtime-api.h
- runtime.cpp (InAccel runtime abstraction layer)
- runtime.h
- kernel_srcs/
- Gradients_0.cpp (Accelerated kernel)
- Gradients_1.cpp (Accelerated kernel)
- Gradients_2.cpp (Accelerated kernel)
- Gradients_3.cpp (Accelerated kernel)
- data/
! Before invoking any of the Makefile targets make sure you have sourced Xilinx XRT setup script.
! Make sure you have set XILINX_SDX environment variable pointing to the SDx installation directory.
As far as the platform (or board) is concerned, Makefile uses AWS_PLATFORM environment variable as the target platform for the kernels compilation. If you are running this on AWS make sure AWS_PLATFORM environment variable is present and points to the platform DSA files1. Otherwise you can set Makefile PLATFORM variable to point to your platform DSA files.
- To obtain the AWS platform DSA files make sure you have cloned the aws-fpga github repository
Download train letters train dataset to data directory. Navigate to data directory and execute the following commands:
wget https://s3.amazonaws.com/inaccel-demo/data/nist/letters_csv_train.dat
wget https://s3.amazonaws.com/inaccel-demo/data/nist/letters_csv_test.datTo compile the kernels for hardware target you just need to execute make xbin_hw while for software and hardware emulation you must execute make xbin_sw and make xbin_hw respectively.
A full list of all the available Makefile targets can be found using make help command.
To test the generated xclbin file you can simply run make host command to create the host application. The host application takes only one input argument, the number of iterations.
Example execution: ./Gradients 100
The above example application spawns a single thread and can train a model using a single FPGA device which is not viable for datacenter-scale needs. Data scientists rely on frameworks like Scikit Learn and Apache Spark to create and test their machine learning pipelines.
InAccel Coral FPGA resource manager is able to automatically scale and schedule any acceleration requests to a cluster of FPGAs, perform load balancing techniques, reconfigure the FPGA devices, perform memory management etc., yet providing a simple to use high level API in Java, CPP and Python.
We have also ready-to-use integrations with broadly used open source frameworks like Apache Spark to seamlessly accelerate your pipelines.
Finally, shaping cutting edge technology, Coral is fully compatible with Kubernetes and using InAccel's device plugin you can set up a Kubernetes cluster aware of hardware accelerated resources or take advantage of Serverless architecture and provide acclerated serverless solutions to your own customers.
- You can create a free InAccel Coral license here.
- You can download InAccel Coral docker from dockerhub.
- You can find full documentation as well as a quick starting guide in InAccel Docs.