NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
Journal:
arXiv
Published Date:
Apr 27, 2025
Abstract
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
31x speedup over Jetson TX2, more than 2x over GPU, 8x speedup over TPU-like
systolic array, and more than 3x over Xilinx DPU. NSFlow also demonstrates
enhanced scalability, with only 4x runtime increase when symbolic workloads
scale by 150x. 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.
