SynapticCore-X: A Modular Neural Processing Architecture for Low-Cost FPGA Acceleration

Abstract

SynapticCore-X is a modular and open-source neural processing architecture optimized for resource-efficient deployment on low-cost FPGA platforms like the Zynq-7020. The design tightly couples a lightweight RV32IMC RISC-V control core with a configurable neural compute tile that supports fused matrix and activation operations. This architecture achieves 100 MHz operation while consuming minimal logic, significantly lowering the barrier to entry for academic research in neural microarchitectures and open-hardware development.

Report

Structured Report: SynapticCore-X

Key Highlights

• Modular Architecture: SynapticCore-X is a modular and resource-efficient neural processing unit (NPU) design.
• Low-Cost Focus: It is specifically optimized for deployment and acceleration on low-cost, commodity FPGA platforms (e.g., validated on PYNQ-Z2).
• Open Source: The entire microarchitecture is provided as fully open-source SystemVerilog, explicitly avoiding reliance on proprietary or heavyweight IP blocks.
• Control Integration: The design utilizes a lightweight RV32IMC RISC-V core to manage the control path.
• Performance/Resource Efficiency: It achieves timing closure at 100 MHz on the Zynq-7020 while consuming extremely low resources (only 6.1% of LUTs).

Technical Details

• Control Plane: Implemented using a lightweight RV32IMC RISC-V control core, responsible for managing execution and data movement.
• Compute Tile: A configurable neural compute tile is the main accelerator block, supporting fused operations including matrix multiplication, activation functions, and efficient data movement.
• Tunability: The architecture parameters are fully tunable, allowing users to rapidly explore design space via configurable parallelism levels, scratchpad memory depth, and DMA burst behavior.
• Implementation Language: The microarchitecture is developed in SystemVerilog.
• Validated Platform & Resources: On the Zynq-7020 FPGA, the design achieved:
  • Clock Frequency: 100 MHz
  • Resource Usage: 6.1% LUTs, 32.5% DSPs, and 21.4% BRAMs.
• Validation: Hardware validation confirms deterministic control-path behavior and cycle-accurate performance for standard matrix and convolution kernels.

Implications

• Democratization of AI Hardware: By providing a complete, open-source, and resource-minimal architecture that runs on commodity educational FPGAs (like PYNQ-Z2), SynapticCore-X drastically lowers the financial and technical barrier for NPU research and prototyping.
• Advancing RISC-V as an Accelerator Host: The successful integration of the lightweight RV32IMC core confirms RISC-V's viability as an efficient, standardized, and open control core for domain-specific accelerators, encouraging further RISC-V hardware-software co-design efforts.
• Enabling Microarchitectural Exploration: The highly modular and tunable nature of the SystemVerilog design facilitates rapid iterative research, allowing hardware developers to quickly test trade-offs in parallelism and memory hierarchy, accelerating innovation in neural microarchitecture design.
• Open Hardware Ecosystem: This contribution adds a critical piece to the open-hardware ecosystem by providing a functional, reproducible, and documented acceleration solution, moving away from reliance on proprietary vendor IP for machine learning acceleration.