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  1. The current state of neuromorphic computing broadly encompasses domain-specific computing architectures designed to accelerate machine learning (ML) and artificial intelligence (AI) algorithms. As is well known, AI/ML algorithms are limited by memory bandwidth. Novel computing architectures are necessary to overcome this limitation. There are several options that are currently under investigation using both mature and emerging memory technologies. For example, mature memory technologies such as high-bandwidth memories (HBMs) are integrated with logic units on the same die to bring memory closer to the computing units. There are also research efforts where in-memory computing architectures have been implemented using DRAMs or flash memory technologies. However, DRAMs suffer from scaling limitations, while flash memory devices suffer from endurance issues. Additionally, in spite of this significant progress, the massive energy consumption needed in neuromorphic processors while meeting the required training and inferencing performance for AI/ML algorithms for future applications needs to be addressed. On the AI/ML algorithm side, there are several pending issues such as life-long learning, explainability, context-based decision making, multimodal association of data, adaptation to address personalized responses, and resiliency. These unresolved challenges in AI/ML have led researchers to explore brain-inspired computing architectures and paradigms.

     
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  2. This work explores the process of adapting the segmented attractor network to a lifelong learning setting. Taking inspirations from Hopfield networks and content-addressable memory, the segmented attractor network is a powerful tool for associative memory applications. The network's performance as an associative memory is analyzed using multiple metrics. In addition to the network's general hit rate, its capability to recall unique memories and their frequency is also evaluated with respect to time. Finally, additional learning techniques are implemented to enhance the network's recall capacity in the application of lifelong learning. These learning techniques are based on human cognitive functions such as memory consolidation, prediction, and forgetting. 
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  3. This work reports a spiking neuromorphic architecture for associative memory simulated in a SPICE environment using recently reported gated-RRAM (resistive random-access memory) devices as synapses alongside neurons based on complementary metal-oxide semiconductors (CMOSs). The network utilizes a Verilog A model to capture the behavior of the gated-RRAM devices within the architecture. The model uses parameters obtained from experimental gated-RRAM devices that were fabricated and tested in this work. Using these devices in tandem with CMOS neuron circuitry, our results indicate that the proposed architecture can learn an association in real time and retrieve the learned association when incomplete information is provided. These results show the promise for gated-RRAM devices for associative memory tasks within a spiking neuromorphic architecture framework. 
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