# LRMP: Layer Replication with Mixed Precision for Spatial In-memory DNN Accelerators

arXiv: https://arxiv.org/abs/2312.03146 · HTML: https://mlsanity.com/paper/2312.03146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [Q-Diffusion: Quantizing Diffusion Models](https://mlsanity.com/paper/2302.04304/) (2023)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference](https://mlsanity.com/paper/1901.10351/) (2019)
- [RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars](https://mlsanity.com/paper/1809.00072/) (2018)
- [TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems](https://mlsanity.com/paper/2002.11151/) (2020)
- [Efficient ConvNets for Analog Arrays](https://mlsanity.com/paper/1807.01356/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Simple Packing Algorithm for Optimized Mapping of Artificial Neural Networks onto Non-Volatile Memory Cross-Bar Arrays](https://mlsanity.com/paper/2411.04814/) (2024)

## Abstract (arXiv)

In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a promising approach to address the rapidly growing computational demands of Deep Neural Networks (DNNs). Mapping DNN layers spatially onto NVM-based IMC accelerators achieves high degrees of parallelism. However, two challenges that arise in this approach are the highly non-uniform distribution of layer processing times and high area requirements. We propose LRMP, a method to jointly apply layer replication and mixed precision quantization to improve the performance of DNNs when mapped to area-constrained NVM-based IMC accelerators. LRMP uses a combination of reinforcement learning and integer linear programming to search the replication-quantization design space using a model that is closely informed by the target hardware architecture. Across five DNN benchmarks, LRMP achieves 2.8-9× latency and 11.8-19× throughput improvement at iso-accuracy.