# An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators

arXiv: https://arxiv.org/abs/2308.12120 · HTML: https://mlsanity.com/paper/2308.12120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration](https://mlsanity.com/paper/1911.09925/) (2019)
- [AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs](https://mlsanity.com/paper/2001.03535/) (2020)
- [HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis](https://mlsanity.com/paper/2009.00871/) (2020)
- [DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors](https://mlsanity.com/paper/2107.09448/) (2021)
- [A Highly Configurable Hardware/Software Stack for DNN Inference Acceleration](https://mlsanity.com/paper/2111.15024/) (2021)
- [NeuPart: Using Analytical Models to Drive Energy-Efficient Partitioning of CNN Computations on Cloud-Connected Mobile Clients](https://mlsanity.com/paper/1905.05011/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Benchmark on Directed Graph Representation Learning in Hardware Designs](https://mlsanity.com/paper/2410.06460/) (2024)
- [PGR-DRC: Pre-Global Routing DRC Violation Prediction Using Unsupervised Learning](https://mlsanity.com/paper/2507.13355/) (2025)

## Abstract (arXiv)

Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It adopts a unified approach that combines backend power, performance, and area (PPA) analysis with frontend performance simulation, thereby achieving a realistic estimation of both backend PPA and system metrics such as runtime and energy. In addition, our framework includes a fully automated DSE technique, which optimizes backend and system metrics through an automated search of architectural and backend parameters. Experimental studies show that our approach consistently predicts backend PPA and system metrics with an average 7% or less prediction error for the ASIC implementation of two deep learning accelerator platforms, VTA and VeriGOOD-ML, in both a commercial 12 nm process and a research-oriented 45 nm process.