Overmind NSA: A Unified Neuro-Symbolic Computing Architecture with Approximate Nonlinear Activations and Preemptive Memory Bypass

2026-04-16

AI Summary

This work addresses the deployment challenges of neuro-symbolic AI—namely high memory overhead, heterogeneous computation patterns, and hardware compatibility issues—by proposing a unified neuro-symbolic computing architecture through hardware-software co-design. The architecture enables efficient integration of perception and structured reasoning by incorporating Padé approximation for tunable-precision evaluation of general nonlinear functions, a prefetch-aware memory bypass mechanism to eliminate on-chip cache bottlenecks, and reconfigurable compute units that dynamically balance accuracy and performance. Experimental results demonstrate that the proposed design achieves 8.1 TOPS/W energy efficiency and 410 GOPS throughput under mixed neuro-symbolic workloads, significantly outperforming existing approaches while incurring negligible model accuracy loss.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Neuro-symbolic AI is gaining traction in domains such as large language models, scientific discovery, and autonomous systems due to its ability to combine perception with structured reasoning. However, its deployment is often constrained by high memory demands, diverse computation patterns, and complex hardware requirements. Existing hardware platforms struggle with large on-chip memory overheads, frequent pipeline stalls, limited I/O bandwidth, and inefficient handling of nonlinear operations. To address these key computational bottlenecks, we propose Overmind, a unified neuro-symbolic architecture with cross-layer optimizations. Overmind tackles these core bottlenecks through Padé approximations for universal nonlinear functions, preemptive memory bypass that eliminates costly on-chip caches, and a complete software stack that optimizes model deployment. By reconfiguring the Padé orders for approximating nonlinear functions, we also demonstrate adaptive accuracy-performance scaling. Overmind achieves an energy efficiency of 8.1 TOPS/W and a throughput of 410 GOPS for mixed neuro-symbolic workloads with minimal model accuracy loss. Compared to existing solutions, Overmind improves performance and efficiency with significantly fewer hardware resources.

Problem

• neuro-symbolic AI
• memory overhead
• nonlinear operations
• hardware efficiency
• I/O bandwidth

Innovation

• Neuro-symbolic architecture
• Padé approximation
• Preemptive memory bypass
• Nonlinear activation
• Hardware-software co-design