Large Language Models (LLMs) have driven remarkable progress in artificial intelligence (AI), but their rapid growth faces challenges of unsustainable computation, limited robustness, and poor explainability. Compositional AI, which integrates LLMs with symbolic reasoning and probabilistic inference, has emerged as a promising paradigm to enable interpretability, robustness, trustworthiness, and data-efficient learning. Recent neuro-symbolic-probabilistic systems demonstrate strong potential in agentic applications, advancing reasoning and cognitive capabilities toward human-like intelligence.

Neuro-symbolic AI systems integrate neural perception with symbolic and probabilistic reasoning to enable dataefficient, interpretable, and robust intelligence beyond purely neural models. Although this compositional paradigm has shown superior performance in domains such as mathematical reasoning, planning, and verification, its deployment remains challenging due to severe inefficiencies in symbolic and probabilistic inference. Through systematic analysis of representative neurosymbolic workloads, we identify probabilistic logical reasoning as the inefficiency bottleneck, characterized by irregular control flow, low arithmetic intensity, uncoalesced memory accesses, and poor hardware utilization on CPUs and GPUs. This paper presents REASON, an integrated acceleration framework for probabilistic logical reasoning in neuro-symbolic AI. At the algorithm level, REASON introduces a unified directed acyclic graph representation that captures common structure across symbolic and probabilistic models, coupled with adaptive pruning and regularization. At the architecture level, REASON features a reconfigurable, tree-based processing fabric optimized for irregular traversal, symbolic deduction, and probabilistic aggregation. At the system level, REASON is tightly integrated with GPU streaming multiprocessors through a programmable interface and multi-level pipeline that efficiently orchestrates neural, symbolic, and probabilistic execution. Evaluated across six neurosymbolic workloads, REASON achieves 1 2 − 5 0 × speedup and 310-681 × energy efficiency over desktop and edge GPUs under TSMC 28 nm node. REASON enables real-time probabilistic logical reasoning, completing end-to-end tasks in 0.8 s with$6 \text{mm}^{2}$area and 2.12 W power, demonstrating that targeted acceleration of probabilistic logical reasoning is critical for practical and scalable neuro-symbolic AI and positioning REASON as a foundational system architecture for next-generation cognitive intelligence.

The rapid progress of artificial intelligence (AI) has been largely driven by deep neural networks, yet their insufficient compositional reasoning abilities, limited robustness, and lack of explainability expose fundamental limitations for next-generation cognitive systems. Energy-Based Models (EBMs), adhering to System-2-style thinking, have recently shown remarkable performance across generative, compositional, and reasoning tasks, offering a pathway toward more robust and cognitively grounded AI. However, the system behavior of EBMs remains poorly understood. Their long sampling loops, strict execution data dependencies, model compositionality and heterogeneity make them highly inefficient on off-the-shelf hardware, creating significant barriers for scalable and real-time deployment. In this paper, we present the first comprehensive workload characterization and system study of EBMs. We first introduce a taxonomy that unifies all the diverse EBM algorithms (around twenty), then formalize their end-to-end runtime to expose key optimization opportunities, and experimentally profile representative models across CPU, GPU, and TPU platforms to understand their runtime breakdowns, memory behavior, computational operators, roofline model, algorithmic performance, and MCMC (Markov Chain Monte Carlo) sampling impacts. Our analysis reveals fundamental system bottlenecks unique to EBMs, including highly repetitive forward and backward neural operations, long and inefficient sampling trajectories, and severe hardware under-utilization caused by heterogeneous model components and operations. Furthermore, based on the experiments and analysis, we suggest optimization solutions across the algorithm, system and architecture levels, to improve EBM computing performance, efficiency, and scalability, aiming to pinpoint the challenges and future directions of EBM research.

Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of AI systems. Recent NSAI systems have gained traction due to their exceptional performance in reasoning tasks and human-AI collaborative scenarios. Despite these algorithmic advancements, executing NSAI tasks on existing hardware (e.g., CPUs, GPUs, TPUs) remains challenging, due to their heterogeneous computing kernels, high memory intensity, and unique memory access patterns. Moreover, current NSAI algorithms exhibit significant variation in operation types and scales, making them incompatible with existing ML accelerators. These challenges highlight the need for a versatile and flexible acceleration framework tailored to NSAI workloads. In this paper, we propose NSFlow, an FPGA-based acceleration framework designed to achieve high efficiency, scalability, and versatility across NSAI systems. NSFlow features a design architecture generator that identifies workload data dependencies and creates optimized dataflow architectures, as well as a reconfigurable array with flexible compute units, re-organizable memory, and mixed-precision capabilities. Evaluating across NSAI workloads, NSFlow achieves $31 \times$ speedup over Jetson TX2, more than $2 \times$ over GPU, $8 \times$ speedup over TPU-like systolic array, and more than $3 \times$ over Xilinx DPU. NSFlow also demonstrates enhanced scalability, with only $4 \times$ runtime increase when symbolic workloads scale by $150 \times$. To the best of our knowledge, NSFlow is the first framework to enable real-time generalizable NSAI algorithms acceleration, demonstrating a promising solution for next-generation cognitive systems.

Neurosymbolic AI is an emerging compositional paradigm that fuses neural learning with symbolic reasoning to enhance the transparency, interpretability, and trustworthiness of AI. It also exhibits higher data efficiency making it promising for edge deployments. Despite the algorithmic promises and demonstrations, unfortunately executing neurosymbolic workloads on current hardware (CPU/GPU/TPU) is challenging due to higher memory intensity, greater compute heterogeneity and access pattern irregularity, leading to severe hardware underutilization. This work proposes CogSys, a characterization and co-design framework dedicated to neurosymbolic AI system acceleration, aiming to win both reasoning efficiency and scalability. On the algorithm side, CogSys proposes an efficient factorization technique to alleviate compute and memory overhead. On the hardware side, CogSys proposes a scalable neurosymbolic architecture with reconfigurable neuro/symbolic processing elements ($n s P E$) and bubble streaming (BS) dataflow with spatial-temporal (ST) mapping for highly parallel and efficient neurosymbolic computation. On the system side, CogSys features an adaptive workload-aware scheduler (adSCH) to orchestrate heterogeneous kernels and enhance resource utilization. Evaluated across cognitive workloads, CogSys enables reconfigurable support for neural and symbolic kernels and exhibits $\gt75 \times$ speedup over TPU-like systolic array with only $\lt5 \%$ area overhead, as benchmarked under the TSMC 28nm technology node. CogSys achieves $4 \times$ $96 \times$ speedup compared to desktop and edge GPUs. For the first time, CogSys enables real-time abduction reasoning towards human fluid intelligence, requiring only 0.3 s per reasoning task with $4 \mathrm{~mm}^{2}$ area and 1.48 W power consumption.

Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems. An elegant approach to represent this intricate factorization is via high-dimensional holographic vectors drawing on brain-inspired vector symbolic architectures. However, holographic factorization involves iterative computation with high-dimensional matrix-vector multiplications and suffers from non-convergence problems. In this paper, we present H3DFact, a heterogeneous 3D integrated in-memory compute engine capable of efficiently factorizing high-dimensional holographic representations. H3DFact exploits the computation-in-superposition capability of holographic vectors and the intrinsic stochasticity associated with memristive-based 3D compute-in-memory. Evaluated on large-scale factorization and perceptual problems, H3DFact demonstrates superior capability in factorization accuracy and operational capacity by up to five orders of magnitude, with 5.5 x compute density, 1.2 x energy efficiency improvements, and 5.9 x less silicon footprint compared to iso-capacity 2D designs.

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 system and architectural perspectives.