Summary of Rule4ml: An Open-source Tool For Resource Utilization and Latency Estimation For Ml Models on Fpga, by Mohammad Mehdi Rahimifar et al.
rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA
by Mohammad Mehdi Rahimifar, Hamza Ezzaoui Rahali, Audrey C. Therrien
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel method introduces a predictive model to estimate the resource utilization and inference latency of Neural Networks (NNs) before their synthesis on Field-Programmable Gate Arrays (FPGAs). The approach leverages HLS4ML, a tool-flow that translates NNs into high-level synthesis (HLS) code, to train regression models for predicting the usage of Block RAM (BRAM), Digital Signal Processors (DSP), Flip-Flops (FF), and Look-Up Tables (LUT), as well as inference clock cycles. The method demonstrated high accuracy on synthetic and existing benchmark architectures, providing valuable preliminary insights for assessing the feasibility and efficiency of NNs on FPGAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to predict how much memory and processing power Neural Networks need before they are built onto special chips called Field-Programmable Gate Arrays. This helps people design these networks more quickly and efficiently. The method uses a tool called HLS4ML to train models that can predict the usage of different parts of the chip, like memory and processors, as well as how long it takes for the network to make predictions. This approach was tested on many different types of neural networks and showed high accuracy. |
Keywords
* Artificial intelligence * Inference * Regression
