# LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning

arXiv: https://arxiv.org/abs/2208.10606 · HTML: https://mlsanity.com/paper/2208.10606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 91 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design](https://mlsanity.com/paper/1907.12952/) (2019)
- [Transfer Learning for Improving Model Predictions in Highly Configurable Software](https://mlsanity.com/paper/1704.00234/) (2017)
- [Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis](https://mlsanity.com/paper/1905.03852/) (2019)
- [NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling](https://mlsanity.com/paper/2009.08241/) (2020)
- [FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications](https://mlsanity.com/paper/2106.06433/) (2021)
- [Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networks](https://mlsanity.com/paper/2002.00190/) (2020)
- [GateKeeper: A New Hardware Architecture for Accelerating Pre-Alignment in DNA Short Read Mapping](https://mlsanity.com/paper/1604.01789/) (2016)
- [Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack](https://mlsanity.com/paper/2004.10854/) (2020)
- [SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping](https://mlsanity.com/paper/2205.05883/) (2022)
- [Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric](https://mlsanity.com/paper/2107.08716/) (2021)
- [SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs](https://mlsanity.com/paper/1910.09020/) (2019)

## Built on by (most cited first, 1 of 1)

- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)

## Abstract (arXiv)

Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design space exploration. First, training requires large amounts of data (features extracted from design synthesis and implementation tools), which is cost-inefficient because of the time-consuming accelerator design and implementation process. Second, a model trained for a specific environment cannot predict performance or resource usage for a new, unknown environment. In a cloud system, renting a platform for data collection to build an ML model can significantly increase the total-cost-ownership (TCO) of a system. Third, ML-based models trained using a limited number of samples are prone to overfitting. To overcome these limitations, we propose LEAPER, a transfer learning-based approach for prediction of performance and resource usage in FPGA-based systems. The key idea of LEAPER is to transfer an ML-based performance and resource usage model trained for a low-end edge environment to a new, high-end cloud environment to provide fast and accurate predictions for accelerator implementation. Experimental results show that LEAPER (1) provides, on average across six workloads and five FPGAs, 85% accuracy when we use our transferred model for prediction in a cloud environment with 5-shot learning and (2) reduces design-space exploration time for accelerator implementation on an FPGA by 10x, from days to only a few hours.