arXiv subjects

Vedant Karia

Publications and source records attributed to Vedant Karia.

4 recordsLinked to original sources

arXiv2025

Genesis: A Spiking Neuromorphic Accelerator With On-chip Continual Learning

Continual learning, the ability to acquire and transfer knowledge through a models lifetime, is critical for artificial agents that interact in real-world environments. Biological brains inherently demonstrate these capabilities while operating within limited energy and resource budgets. Achieving continual learning capability in artificial systems considerably increases memory and computational demands, and even more so when deploying on platforms with limited resources. In this work, Genesis, a spiking continual learning accelerator, is proposed to address this gap. The architecture supports neurally inspired mechanisms, such as activity-dependent metaplasticity, to alleviate catastrophic forgetting. It integrates low-precision continual learning parametersand employs a custom data movement strategy to accommodate the sparsely distributed spikes. Furthermore, the architecture features a memory mapping technique that places metaplasticity parameters and synaptic weights in a single address location for faster memory access. Results show that the mean classification accuracy for Genesis is 74.6% on a task-agnostic split-MNIST benchmark with power consumption of 17.08mW in a 65nm technology node.

cs.NE↗

arXiv2025

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

cs.AI↗

arXiv2024

Probabilistic Metaplasticity for Continual Learning with Memristors

Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning entails memory and computational overhead. Crossbar architectures using memristor devices offer energy efficiency through compute-in-memory and hold promise to address this issue. However, memristors often exhibit low precision and high variability in conductance modulation, rendering them unsuitable for continual learning solutions that require precise modulation of weight magnitude for consolidation. Current approaches fall short to address this challenge directly and rely on auxiliary high-precision memory, leading to frequent memory access, high memory overhead, and energy dissipation. In this research, we propose probabilistic metaplasticity, which consolidates weights by modulating their update probability rather than magnitude. The proposed mechanism eliminates high-precision modification to weight magnitudes and, consequently, the need for auxiliary high-precision memory. We demonstrate the efficacy of the proposed mechanism by integrating probabilistic metaplasticity into a spiking network trained on an error threshold with low-precision memristor weights. Evaluations of continual learning benchmarks show that probabilistic metaplasticity achieves performance equivalent to state-of-the-art continual learning models with high-precision weights while consuming ~ 67% lower memory for additional parameters and up to ~ 60x lower energy during parameter updates compared to an auxiliary memory-based solution. The proposed model shows potential for energy-efficient continual learning with low-precision emerging devices.

eess.SY↗

arXiv2021

TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT

In this research, we propose a new low-precision framework, TENT, to leverage the benefits of a tapered fixed-point numerical format in TinyML models. We introduce a tapered fixed-point quantization algorithm that matches the numerical format's dynamic range and distribution to that of the deep neural network model's parameter distribution at each layer. An accelerator architecture for the tapered fixed-point with TENT framework is proposed. Results show that the accuracy on classification tasks improves up to ~31 % with an energy overhead of ~17-30 % as compared to fixed-point, for ConvNet and ResNet-18 models.

cs.LG↗