Modeling and Optimizing Performance Bottlenecks for Neuromorphic Accelerators

2025-11-26

AI Summary

Traditional sparsity and operand metrics fail to accurately reflect actual performance bottlenecks in neuromorphic accelerators. Method: This paper introduces the first systematic performance bound and bottleneck analysis framework tailored to neuromorphic architectures. It formally defines three bottleneck regimes—memory-bound, compute-bound, and traffic-bound—and constructs a sparsity-aware visual floorline model. Integrating theoretical modeling with empirical hardware validation, it proposes a floorline-driven workload partitioning strategy and a sparsity-aware training methodology. Contribution/Results: Under zero-accuracy-loss constraints, the approach achieves up to 3.86× speedup and 3.38× energy reduction over manual tuning. It rigorously exposes and overcomes fundamental limitations of existing optimization paradigms, enabling principled, architecture-aware co-design of algorithms and hardware for neuromorphic computing.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Neuromorphic accelerators offer promising platforms for machine learning (ML) inference by leveraging event-driven, spatially-expanded architectures that naturally exploit unstructured sparsity through co-located memory and compute. However, their unique architectural characteristics create performance dynamics that differ fundamentally from conventional accelerators. Existing workload optimization approaches for neuromorphic accelerators rely on aggregate network-wide sparsity and operation counting, but the extent to which these metrics actually improve deployed performance remains unknown. This paper presents the first comprehensive performance bound and bottleneck analysis of neuromorphic accelerators, revealing the shortcomings of the conventional metrics and offering an understanding of what facets matter for workload performance. We present both theoretical analytical modeling and extensive empirical characterization of three real neuromorphic accelerators: Brainchip AKD1000, Synsense Speck, and Intel Loihi 2. From these, we establish three distinct accelerator bottleneck states, memory-bound, compute-bound, and traffic-bound, and identify which workload configuration features are likely to exhibit these bottleneck states. We synthesize all of our insights into the floorline performance model, a visual model that identifies performance bounds and informs how to optimize a given workload, based on its position on the model. Finally, we present an optimization methodology that combines sparsity-aware training with floorline-informed partitioning. Our methodology achieves substantial performance improvements at iso-accuracy: up to 3.86x runtime improvement and 3.38x energy reduction compared to prior manually-tuned configurations.

Problem

• Analyzing performance bottlenecks in neuromorphic accelerators' unique architectures
• Identifying shortcomings of conventional sparsity metrics for workload optimization
• Developing optimization methodology for improved runtime and energy efficiency

Innovation

• Developed floorline performance model for bottlenecks
• Introduced sparsity-aware training with partitioning optimization
• Identified three accelerator bottleneck states theoretically empirically