# Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives

arXiv: https://arxiv.org/abs/2307.03936 · HTML: https://mlsanity.com/paper/2307.03936/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 73 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Compressing Deep Convolutional Networks using Vector Quantization](https://mlsanity.com/paper/1412.6115/) (2014)
- [Quantization Networks](https://mlsanity.com/paper/1911.09464/) (2019)
- [A Survey on Methods and Theories of Quantized Neural Networks](https://mlsanity.com/paper/1808.04752/) (2018)
- [PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference](https://mlsanity.com/paper/1901.10351/) (2019)
- [ProxQuant: Quantized Neural Networks via Proximal Operators](https://mlsanity.com/paper/1810.00861/) (2018)
- [HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs](https://mlsanity.com/paper/2007.09952/) (2020)
- [PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM](https://mlsanity.com/paper/1912.11516/) (2019)
- [Restructuring Batch Normalization to Accelerate CNN Training](https://mlsanity.com/paper/1807.01702/) (2018)
- [A Learning Framework for n-bit Quantized Neural Networks toward FPGAs](https://mlsanity.com/paper/2004.02396/) (2020)
- [High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS](https://mlsanity.com/paper/1909.07514/) (2019)
- [Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism](https://mlsanity.com/paper/1810.12154/) (2018)
- [Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors](https://mlsanity.com/paper/2009.01588/) (2020)
- [SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks](https://mlsanity.com/paper/2108.08903/) (2021)
- [Additive Noise Annealing and Approximation Properties of Quantized Neural Networks](https://mlsanity.com/paper/1905.10452/) (2019)
- [FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks](https://mlsanity.com/paper/2201.07634/) (2022)
- [A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks](https://mlsanity.com/paper/2206.08735/) (2022)

## Built on by (most cited first, 5 of 5)

- [Mathematical Formalism for Memory Compression in Selective State Space Models](https://mlsanity.com/paper/2410.03158/) (2024)
- [Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization](https://mlsanity.com/paper/2410.16759/) (2024)
- [On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels](https://mlsanity.com/paper/2502.18623/) (2025)
- [CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search](https://mlsanity.com/paper/2509.25862/) (2025)
- [Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators](https://mlsanity.com/paper/2603.03880/) (2026)

## Abstract (arXiv)

The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques are applied to implement a neural network on limited hardware resources. Quantization is one of the most efficient network compression techniques allowing to reduce the memory footprint, latency, and energy consumption. This paper provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and links software-based quantization approaches to IMC hardware implementation. Moreover, open challenges, QNN design requirements, recommendations, and perspectives along with an IMC-based QNN hardware roadmap are provided.