# Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators

arXiv: https://arxiv.org/abs/1911.00139 · HTML: https://mlsanity.com/paper/1911.00139/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 30 of 32)

- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices](https://mlsanity.com/paper/2202.11317/) (2022)
- [Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search](https://mlsanity.com/paper/2107.06871/) (2021)
- [SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2202.08395/) (2022)
- [Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow](https://mlsanity.com/paper/2109.03430/) (2021)
- [Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators](https://mlsanity.com/paper/2106.06575/) (2021)
- [Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?](https://mlsanity.com/paper/2207.07626/) (2022)
- [Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures](https://mlsanity.com/paper/2405.04700/) (2024)
- [FLASH: Fast Neural Architecture Search with Hardware Optimization](https://mlsanity.com/paper/2108.00568/) (2021)
- [Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs](https://mlsanity.com/paper/2109.03806/) (2021)
- [On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators](https://mlsanity.com/paper/2306.06923/) (2023)
- [AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing](https://mlsanity.com/paper/2305.10459/) (2023)
- [Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices](https://mlsanity.com/paper/2102.06336/) (2021)
- [RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search](https://mlsanity.com/paper/2109.05691/) (2021)
- [Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise](https://mlsanity.com/paper/2307.15853/) (2023)
- [XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing](https://mlsanity.com/paper/2303.17646/) (2023)
- [NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems](https://mlsanity.com/paper/2106.12125/) (2021)
- [EPIM: Efficient Processing-In-Memory Accelerators based on Epitome](https://mlsanity.com/paper/2311.07620/) (2023)
- [Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections](https://mlsanity.com/paper/2312.06137/) (2023)
- [TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators](https://mlsanity.com/paper/2406.06544/) (2024)
- [RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms](https://mlsanity.com/paper/2111.02569/) (2021)
- [NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators](https://mlsanity.com/paper/2305.14561/) (2023)
- [Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization](https://mlsanity.com/paper/2410.16759/) (2024)
- [TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection](https://mlsanity.com/paper/2305.05105/) (2023)
- [Multi-Objective Neural Architecture Search for In-Memory Computing](https://mlsanity.com/paper/2406.06746/) (2024)
- [AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory Computing](https://mlsanity.com/paper/2506.18495/) (2025)
- [CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search](https://mlsanity.com/paper/2509.25862/) (2025)
- [U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2401.05357/) (2023)
- [CiMRAG: CiM-Aware Domain-Adaptive and Noise-Resilient Retrieval-Augmented Generation for Edge-Based LLMs](https://mlsanity.com/paper/2601.20041/) (2026)
- [Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators](https://mlsanity.com/paper/2603.03880/) (2026)

## Abstract (arXiv)

Co-exploration of neural architectures and hardware design is promising to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the-art neural architecture search algorithms for the co-exploration are dedicated for the conventional von-neumann computing architecture, whose performance is heavily limited by the well-known memory wall. In this paper, we are the first to bring the computing-in-memory architecture, which can easily transcend the memory wall, to interplay with the neural architecture search, aiming to find the most efficient neural architectures with high network accuracy and maximized hardware efficiency. Such a novel combination makes opportunities to boost performance, but also brings a bunch of challenges. The design space spans across multiple layers from device type, circuit topology to neural architecture. In addition, the performance may degrade in the presence of device variation. To address these challenges, we propose a cross-layer exploration framework, namely NACIM, which jointly explores device, circuit and architecture design space and takes device variation into consideration to find the most robust neural architectures. Experimental results demonstrate that NACIM can find the robust neural network with 0.45% accuracy loss in the presence of device variation, compared with a 76.44% loss from the state-of-the-art NAS without consideration of variation; in addition, NACIM achieves an energy efficiency up to 16.3 TOPs/W, 3.17X higher than the state-of-the-art NAS.