# Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability

arXiv: https://arxiv.org/abs/2504.09851 · HTML: https://mlsanity.com/paper/2504.09851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 14 Apr 2025.

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

- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU](https://mlsanity.com/paper/2002.09481/) (2020)
- [ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference](https://mlsanity.com/paper/2209.04161/) (2022)
- [DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators](https://mlsanity.com/paper/2201.11220/) (2022)
- [Positive/Negative Approximate Multipliers for DNN Accelerators](https://mlsanity.com/paper/2107.09366/) (2021)
- [ECO-CHIP: Estimation of Carbon Footprint of Chiplet-based Architectures for Sustainable VLSI](https://mlsanity.com/paper/2306.09434/) (2023)
- [Chiplet-Gym: Optimizing Chiplet-based AI Accelerator Design with Reinforcement Learning](https://mlsanity.com/paper/2406.00858/) (2024)
- [Late Breaking Results: Leveraging Approximate Computing for Carbon-Aware DNN Accelerators](https://mlsanity.com/paper/2502.00286/) (2025)
- [Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems](https://mlsanity.com/paper/2305.01831/) (2023)
- [3D-Carbon: An Analytical Carbon Modeling Tool for 3D and 2.5D Integrated Circuits](https://mlsanity.com/paper/2307.08060/) (2023)
- [Leveraging Highly Approximated Multipliers in DNN Inference](https://mlsanity.com/paper/2412.16757/) (2024)

## Abstract (arXiv)

As Deep Neural Networks (DNNs) continue to drive advancements in artificial intelligence, the design of hardware accelerators faces growing concerns over embodied carbon footprint due to complex fabrication processes. 3D integration improves performance but introduces sustainability challenges, making carbon-aware optimization essential. In this work, we propose a carbon-efficient design methodology for 3D DNN accelerators, leveraging approximate computing and genetic algorithm-based design space exploration to optimize Carbon Delay Product (CDP). By integrating area-efficient approximate multipliers into Multiply-Accumulate (MAC) units, our approach effectively reduces silicon area and fabrication overhead while maintaining high computational accuracy. Experimental evaluations across three technology nodes (45nm, 14nm, and 7nm) show that our method reduces embodied carbon by up to 30% with negligible accuracy drop.