mlsanityData to 14 Oct 2026

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

arXiv 2307.03936 ↗First posted 8 Jul 2023cs.AR cs.AI cs.ETcited by 5

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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.

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

18 of 73 references.

References · 18

  1. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  2. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 592
  3. 2014Compressing Deep Convolutional Networks using Vector Quantizationcited by 195
  4. 2019Quantization Networkscited by 153
  5. 2018A Survey on Methods and Theories of Quantized Neural Networkscited by 75
  6. 2019PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inferencecited by 61
  7. 2018ProxQuant: Quantized Neural Networks via Proximal Operatorscited by 59
  8. 2020HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNscited by 43
  9. 2019PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAMcited by 17
  10. 2018Restructuring Batch Normalization to Accelerate CNN Trainingcited by 12
  11. 2020A Learning Framework for n-bit Quantized Neural Networks toward FPGAscited by 12
  12. 2019High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOScited by 10
6 more
  1. 2018Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanismcited by 8
  2. 2020Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectorscited by 8
  3. 2021SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networkscited by 6
  4. 2019Additive Noise Annealing and Approximation Properties of Quantized Neural Networkscited by 5
  5. 2022FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networkscited by 2
  6. 2022A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024Mathematical Formalism for Memory Compression in Selective State Space Modelscited by 2
  2. 2024Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimizationcited by 2
  3. 2025On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levelscited by 2
  4. 2025CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Searchcited by 1
  5. 2026Joint Hardware-Workload Co-Optimization for In-Memory Computing Acceleratorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications
  2. 2021Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
  3. 2023On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks
  4. 2024BF-IMNA: A Bit Fluid In-Memory Neural Architecture for Neural Network Acceleration
  5. 2025Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications
  6. 2023A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
  7. 2026Trainable Bitwise Soft Quantization for Input Feature Compression
  8. 2022AMED: Automatic Mixed-Precision Quantization for Edge Devices