# FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications

arXiv: https://arxiv.org/abs/2106.06433 · HTML: https://mlsanity.com/paper/2106.06433/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2021.

## Built on by (most cited first, 8 of 8)

- [Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2206.06022/) (2022)
- [An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2207.07886/) (2022)
- [ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis](https://mlsanity.com/paper/2207.09765/) (2022)
- [From Loop Nests to Silicon: Mapping AI Workloads onto AMD NPUs with MLIR-AIR](https://mlsanity.com/paper/2510.14871/) (2025)
- [EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators](https://mlsanity.com/paper/2202.02310/) (2022)
- [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022)
- [LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning](https://mlsanity.com/paper/2208.10606/) (2022)
- [Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage](https://mlsanity.com/paper/2603.14583/) (2026)

## Abstract (arXiv)

Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both execution cycles and energy in current computing systems due to the costly data movement between the computation units and the memory units. Genome analysis and weather prediction are two examples of such applications. Recent FPGAs couple a reconfigurable fabric with high-bandwidth memory (HBM) to enable more efficient data movement and improve overall performance and energy efficiency. This trend is an example of a paradigm shift to near-memory computing. We leverage such an FPGA with high-bandwidth memory (HBM) for improving the pre-alignment filtering step of genome analysis and representative kernels from a weather prediction model. Our evaluation demonstrates large speedups and energy savings over a high-end IBM POWER9 system and a conventional FPGA board with DDR4 memory. We conclude that FPGA-based near-memory computing has the potential to alleviate the data movement bottleneck for modern data-intensive applications.