Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture (2409.13153v2)

Abstract: The remarkable advancements in AI, primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-generation cognitive AI systems, neuro-symbolic AI emerges as a promising paradigm, fusing neural and symbolic approaches to enhance interpretability, robustness, and trustworthiness, while facilitating learning from much less data. Recent neuro-symbolic systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we aim to understand the workload characteristics and potential architectures for neuro-symbolic AI. We first systematically categorize neuro-symbolic AI algorithms, and then experimentally evaluate and analyze them in terms of runtime, memory, computational operators, sparsity, and system characteristics on CPUs, GPUs, and edge SoCs. Our studies reveal that neuro-symbolic models suffer from inefficiencies on off-the-shelf hardware, due to the memory-bound nature of vector-symbolic and logical operations, complex flow control, data dependencies, sparsity variations, and limited scalability. Based on profiling insights, we suggest cross-layer optimization solutions and present a hardware acceleration case study for vector-symbolic architecture to improve the performance, efficiency, and scalability of neuro-symbolic computing. Finally, we discuss the challenges and potential future directions of neuro-symbolic AI from both system and architectural perspectives.

• The paper systematically categorizes neuro-symbolic AI workloads into five paradigms and analyzes their runtime and memory efficiency.
• It identifies key inefficiencies in hardware utilization, highlighting issues like low ALU usage, poor cache hit rates, and memory-bound symbolic operations.
• The authors propose a hardware accelerator design that optimizes data flow and heterogeneous processing to reduce latency and energy consumption.

Cross-Layer Design for Neuro-Symbolic AI: From Workload Characterization to Hardware Acceleration

Overview

The paper addresses the computational challenges faced by contemporary AI systems, predominantly driven by deep neural networks. As these systems grow, they face limitations in explainability, robustness, and scalability, leading to increased demand for neuro-symbolic AI frameworks. Neuro-symbolic models promise improved interpretability and trustworthiness while ensuring efficient data utilization, making them an attractive alternative for next-generation AI systems.

Characterization of Neuro-Symbolic Workloads

The authors emphasize the need to understand neuro-symbolic workloads to develop efficient architectures. They systematically categorize existing neuro-symbolic AI algorithms into five paradigms: (1) Symbolic[Neuro], (2) Neural|Symbolic, (3) Neuro:Symbolic$\rightarrow$Neuro, (4) $\text{Neuro}_{\text{Symbolic}}$, and (5) Neuro[Symbolic]. Each category integrates neural and symbolic elements differently, influencing the system's overall computational pattern. The paper evaluates several representative workloads, including Logical Neural Networks and Vector Symbolic Architectures (VSA), analyzing their runtime, memory demands, and computational characteristics across different platforms.

Key Observations

1. Runtime and Computational Inefficiencies: Neuro-symbolic models often show high latency, particularly in symbolic components, which can dominate system runtime and become bottlenecks.
2. Hardware Utilization: Due to the memory-bound nature of symbolic operations, existing hardware like CPUs and GPUs is underutilized when executing neuro-symbolic workloads. This inefficiency arises due to low ALU utilization, low cache hit rates, and high data movement.
3. Memory and Scalability: Symbolic operations exhibit higher memory intensity, leading to scalability issues, particularly with increasing task complexity.
4. Sparsity and Data Dependencies: These models demonstrate unstructured sparsity and entail complex data dependencies between neural and symbolic components, further complicating hardware execution and optimizations.

Practical and Theoretical Implications

The profiling insights guide several cross-layer optimization strategies. The authors propose a hardware accelerator case paper focusing on vs vector-symbolic architectures. The design incorporates energy-efficient data flow, heterogeneous processing units, and reduced memory footprint, leading to significant improvements in latency and energy efficiency compared to conventional GPUs.

Recommendations and Future Directions

For realizing the full potential of neuro-symbolic AI, the authors recommend:

1. Developing large, challenging datasets akin to ImageNet for neuro-symbolic models to advance their cognitive abilities.
2. Creating a unified framework to seamlessly integrate neural, symbolic, and probabilistic approaches.
3. Developing efficient software frameworks to enhance the modularity and extendability of neuro-symbolic systems.
4. Designing benchmarks that reflect the diverse characteristics of neuro-symbolic workloads, guiding the development of optimized architectures.
5. Innovating cognitive hardware architectures that address the specific needs of neuro-symbolic operations, offering flexibility and efficiency.

Conclusion

This paper is an initial step toward understanding and optimizing neuro-symbolic AI systems. The researchers aim to inspire future developments in this domain through collaborative advancements in algorithms, systems, architecture, and co-design techniques, ultimately fostering the design of next-generation cognitive computing systems.