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1. Neuromorphic Swarm on RRAM Compute-in-Memory Processor for Solving QUBO Problem

   https://doi.org/10.1109/DAC56929.2023.10247852  

   Lele, Ashwin Sanjay; Chang, Muya; Spetalnick, Samuel D.; Crafton, Brian; Raychowdhury, Arijit; Fang, Yan (July 2023, IEEE)

   Combinatorial optimization problems prevail in engineering and industry. Some are NP-hard and thus become difficult to solve on edge devices due to limited power and computing resources. Quadratic Unconstrained Binary Optimization (QUBO) problem is a valuable emerging model that can formulate numerous combinatorial problems, such as Max-Cut, traveling salesman problems, and graphic coloring. QUBO model also reconciles with two emerging computation models, quantum computing and neuromorphic computing, which can potentially boost the speed and energy efficiency in solving combinatorial problems. In this work, we design a neuromorphic QUBO solver composed of a swarm of spiking neural networks (SNN) that conduct a population-based meta-heuristic search for solutions. The proposed model can achieve about x20 40 speedup on large QUBO problems in terms of time steps compared to a traditional neural network solver. As a codesign, we evaluate the neuromorphic swarm solver on a 40nm 25mW Resistive RAM (RRAM) Compute-in-Memory (CIM) SoC with a 2.25MB RRAM-based accelerator and an embedded Cortex M3 core. The collaborative SNN swarm can fully exploit the specialty of CIM accelerator in matrix and vector multiplications. Compared to previous works, such an algorithm-hardware synergized solver exhibits advantageous speed and energy efficiency for edge devices. 

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2. Realizing Behavior Level Associative Memory Learning Through Three-Dimensional Memristor-Based Neuromorphic Circuits

   https://doi.org/10.1109/TETCI.2019.2921787  

   An, Hongyu; An, Qiyuan; Yi, Yang (July 2019, IEEE Transactions on Emerging Topics in Computational Intelligence)

   Associative memory is a widespread self-learning method in biological livings, which enables the nervous system to remember the relationship between two concurrent events. The significance of rebuilding associative memory at a behavior level is not only to reveal a way of designing a brain-like self-learning neuromorphic system but also to explore a method of comprehending the learning mechanism of a nervous system. In this paper, an associative memory learning at a behavior level is realized that successfully associates concurrent visual and auditory information together (pronunciation and image of digits). The task is achieved by associating the large-scale artificial neural networks (ANNs) together instead of relating multiple analog signals. In this way, the information carried and preprocessed by these ANNs can be associated. A neuron has been designed, named signal intensity encoding neurons (SIENs), to encode the output data of the ANNs into the magnitude and frequency of the analog spiking signals. Then, the spiking signals are correlated together with an associative neural network, implemented with a three-dimensional (3-D) memristor array. Furthermore, the selector devices in the traditional memristor cells limiting the design area have been avoided by our novel memristor weight updating scheme. With the novel SIENs, the 3-D memristive synapse, and the proposed memristor weight updating scheme, the simulation results demonstrate that our proposed associative memory learning method and the corresponding circuit implementations successfully associate the pronunciation and image of digits together, which mimics a human-like associative memory learning behavior. 

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3. Replicating associative learning of rodents with a neuromorphic robot in an open-field arena

   https://doi.org/10.3389/fnins.2025.1565780  

   Liu, Tianze; Bai, Kang Jun; An, Hongyu (June 2025, Frontiers in Neuroscience)

   Machado, Pedro (Ed.)

   This study emulates associative learning in rodents by using a neuromorphic robot navigating an open-field arena. The goal is to investigate how biologically inspired neural models can reproduce animal-like learning behaviors in real-world robotic systems. We constructed a neuromorphic robot by deploying computational models of spatial and sensory neurons onto a mobile platform. Different coding schemes—rate coding for vibration signals and population coding for visual signals—were implemented. The associative learning model employs 19 spiking neurons and follows Hebbian plasticity principles to associate visual cues with favorable or unfavorable locations. Our robot successfully replicated classical rodent associative learning behavior by memorizing causal relationships between environmental cues and spatial outcomes. The robot’s self-learning capability emerged from repeated exposure and synaptic weight adaptation, without the need for labeled training data. Experiments confirmed functional learning behavior across multiple trials. This work provides a novel embodied platform for memory and learning research beyond traditional animal models. By embedding biologically inspired learning mechanisms into a real robot, we demonstrate how spatial memory can be formed and expressed through sensorimotor interactions. The model’s compact structure (19 neurons) illustrates a minimal yet functional learning network, and the study outlines principles for synaptic weight and threshold design, guiding future development of more complex neuromorphic systems. 

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4. Spiking Transformer Hardware Accelerators in 3D Integration

   Xu, Boxun; Hwang, Junyoung; Vanna-iampikul, Pruek; Lim, Sung_Kyu; Li, Peng (October 2024, IEEE/ACM International Conference on Computer-Aided Design (ICCAD ’24))

   Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention mechanisms similar to those found in their artificial neural network counterparts, recently emerged spiking transformers have showcased promising performance and efficiency by capitalizing on the binary nature of spiking operations. Recognizing the current lack of dedicated hardware support for spiking transformers, this paper presents the first work on 3D spiking transformer hardware architecture and design methodology. We present an architecture and physical design co-optimization approach tailored specifically for spiking transformers. Through memory-on-logic and logic-on-logic stacking enabled by 3D integration, we demonstrate significant energy and delay improvements compared to conventional 2D CMOS integration. 

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5. Signature Driven Post-Manufacture Testing and Tuning of RRAM Spiking Neural Networks for Yield Recovery

   Saha, A; Amarnath, C; Ma, K; Chatterjee, A (May 2025, Proceeding, IEEE/SIGDA Asian South Pacific Design Automation Conference)

   IEEE (Ed.)

   Resistive random access Memory (RRAM) based spiking neural networks (SNN) are becoming increasingly attractive for pervasive energy-efficient classification tasks. However, such networks suffer from degradation of performance (as determined by classification accuracy) due to the effects of process variations on fabricated RRAM devices resulting in loss of manufacturing yield. To address such yield loss, a two-step approach is developed. First, an alternative test framework is used to predict the performance of fabricated RRAM based SNNs using the SNN response to a small subset of images from the test image dataset, called the SNN response signature (to minimize test cost). This diagnoses those SNNs that need to be performance-tuned for yield recovery. Next, SNN tuning is performed by modulating the spiking thresholds of the SNN neurons on a layer-by-layer basis using a trained regressor that maps the SNN response signature to the optimal spiking thresholdvalues during tuning. The optimal spiking threshold values are determined by an off-line optimization algorithm. Experiments show that the proposed framework can reduce the number of out-of-spec SNN devices by up to 54% and improve yield by as much as 8.6%. 

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