Towards Cognitive AI Systems: Workload and Characterization of Neuro-Symbolic AI
Abstract
The remarkable advancements in artificial intel-ligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational tra-jectories, 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 trustwor-thiness, 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 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 syste…
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Symbolic components can introduce faithfulness and reliability into a neural system that cannot be achieved by an LLM alone.
“Symbolic components can introduce faithfulness and reliability into a neural system that cannot be achieved by an LLM alone [20].”
From ProSLM: A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering (2024) · cites this paper as [20]
Cites
Explainable AI (XAI): Core Ideas, Techniques, and Solutions
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., et al. (2023) · ACM Computing Surveys · doi:10.1145/3561048 · Cited by 1,386 · see citing works
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
Gu, Q., Kuwajerwala, A., Morin, S., Jatavallabhula, K. M., Sen, B., Agarwal, A., Rivera, C., Paul, W., Ellis, K., Chellappa, R., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610243 · Cited by 255 · cited 1× · see citing works
“In addition to its superior performance in vision and language [9]–[11], neuro-symbolic AI holds sig-268 nificant potential for enhancing explainability and trustworthiness of collaborative human-AI applications [12]–[14].”
Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.01500 · Cited by 206 · see citing works
Heterogeneous Dataflow Accelerators for Multi-DNN Workloads
Kwon, H., Lai, L., Pellauer, M., Krishna, T., Chen, Y.-H., & Chandra, V. (2021) · 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca51647.2021.00016 · Cited by 144 · see citing works
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Why we need to be careful with LLMs in medicine
Bélisle-Pipon, J.-C. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1495582 · Cited by 91 · see citing works
Vakharia, P., Kufeldt, A., Meyers, M., Lane, I., & Gilpin, L. H. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-71170-1_23 · Cited by 5 · see citing works
- Authors
- Wan, Z., Liu, C.-K., Yang, H., Raj, R., Li, C., You, H., Fu, Y., Wan, C., Samajdar, A., Lin, Y. C., Krishna, T., & Raychowdhury, A.
- Affiliations
- Georgia Institute of Technology (US)
- IBM (United States) (US)
- ORCID
- Venue
- 2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
- Publisher
- IEEE
- Published
- 2024-05-05
- Pages
- 268-279
- OpenAlex
- W4400681438
- Cited by
- 39
- References
- 4
- Citations by year
- 2026: 16 · 2025: 12 · 2024: 11
- FWCI
- 11.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Keywords
- neuro-symbolic AI, workload characterization, hyperdimensional computing, logical operations, cross-layer optimization, hardware efficiency, scalability, explainability, human-AI collaboration
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