# QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators

arXiv: https://arxiv.org/abs/2205.08648 · HTML: https://mlsanity.com/paper/2205.08648/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Towards Efficient Model Compression via Learned Global Ranking](https://mlsanity.com/paper/1904.12368/) (2019)
- [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks](https://mlsanity.com/paper/2002.04116/) (2020)
- [NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks](https://mlsanity.com/paper/1710.05420/) (2017)
- [Accelerator-aware Neural Network Design using AutoML](https://mlsanity.com/paper/2003.02838/) (2020)
- [SCALE-Sim: Systolic CNN Accelerator Simulator](https://mlsanity.com/paper/1811.02883/) (2018)
- [The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems](https://mlsanity.com/paper/2012.04210/) (2020)
- [DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning](https://mlsanity.com/paper/2012.04559/) (2020)

## Built on by (most cited first, 4 of 4)

- [QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality](https://mlsanity.com/paper/2205.13045/) (2022)
- [QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration](https://mlsanity.com/paper/2206.15463/) (2022)
- [Efficient Deep Learning Using Non-Volatile Memory Technology](https://mlsanity.com/paper/2206.13601/) (2022)
- [A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures](https://mlsanity.com/paper/2311.17815/) (2023)

## Abstract (arXiv)

As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that incorporates quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QAPPA, a highly parameterized quantization-aware power, performance, and area modeling framework for DNN accelerators. Our framework can facilitate the future research on design space exploration of DNN accelerators for various design choices such as bit precision, processing element type, scratchpad sizes of processing elements, global buffer size, device bandwidth, number of total processing elements in the the design, and DNN workloads. Our results show that different bit precisions and processing element types lead to significant differences in terms of performance per area and energy. Specifically, our proposed lightweight processing elements achieve up to 4.9x more performance per area and energy improvement when compared to INT16 based implementation.