# Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units

arXiv: https://arxiv.org/abs/2403.11563 · HTML: https://mlsanity.com/paper/2403.11563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review](https://mlsanity.com/paper/2202.08897/) (2022)
- [Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision](https://mlsanity.com/paper/2110.02929/) (2021)
- [Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations](https://mlsanity.com/paper/1709.06206/) (2017)
- [Kraken: A Direct Event/Frame-Based Multi-sensor Fusion SoC for Ultra-Efficient Visual Processing in Nano-UAVs](https://mlsanity.com/paper/2209.01065/) (2022)
- [A Survey of Spiking Neural Network Accelerator on FPGA](https://mlsanity.com/paper/2307.03910/) (2023)
- [An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA](https://mlsanity.com/paper/2301.07050/) (2023)
- [Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks](https://mlsanity.com/paper/2307.06084/) (2023)
- [A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks](https://mlsanity.com/paper/2204.02942/) (2022)
- [Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks](https://mlsanity.com/paper/2309.08232/) (2023)
- [NEUROSEC: FPGA-Based Neuromorphic Audio Security](https://mlsanity.com/paper/2401.12055/) (2024)
- [BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC](https://mlsanity.com/paper/2211.11331/) (2022)

## Abstract (arXiv)

This paper introduces a groundbreaking digital neuromorphic architecture that innovatively integrates Brain Code Unit (BCU) and Fundamental Code Unit (FCU) using mixedsignal design methodologies. Leveraging open-source datasets and the latest advances in materials science, our research focuses on enhancing the computational efficiency, accuracy, and adaptability of neuromorphic systems. The core of our approach lies in harmonizing the precision and scalability of digital systems with the robustness and energy efficiency of analog processing. Through experimentation, we demonstrate the effectiveness of our system across various metrics. The BCU achieved an accuracy of 88.0% and a power efficiency of 20.0 GOP/s/W, while the FCU recorded an accuracy of 86.5% and a power efficiency of 18.5 GOP/s/W. Our mixed-signal design approach significantly improved latency and throughput, achieving a latency as low as 0.75 ms and throughput up to 213 TOP/s. These results firmly establish the potential of our architecture in neuromorphic computing, providing a solid foundation for future developments in this domain. Our study underscores the feasibility of mixedsignal neuromorphic systems and their promise in advancing the field, particularly in applications requiring high efficiency and adaptability