Abstract

This tutorial paper outlines the architectural design principles for scalable digital neuromorphic processors suitable for low-power EdgeAI applications, using the SENECA platform as a primary example. The approach starts with flexible arrays of tiny RISC-V cores connected by a Network-on-Chip (NoC) and progressively evolves the structure by integrating dedicated Neural Processing Elements (NPEs) and specialized loop controllers. The work emphasizes crucial architectural trade-offs, performance bottlenecks, and software techniques like spike grouping and event-driven depth-first convolution necessary for domain-specific acceleration.

Report

Structured Report: Building Scalable Digital Neuromorphic Processors

Key Highlights

• Focus Area: The paper provides a tutorial and synthesis of architectural findings for building fully digital, scalable neuromorphic processors targeting low-power, always-on EdgeAI applications.
• Platform Example: The SENECA platform is used as the running example to illustrate the step-by-step evolution of the architecture.
• RISC-V Foundation: The baseline design utilizes an array of tiny RISC-V processing cores interconnected via a simple Network-on-Chip (NoC), leveraging the flexibility of General-Purpose Processors (GPPs).
• Architectural Evolution: The tutorial demonstrates how to incrementally add domain-specific acceleration, specifically by introducing dedicated Neural Processing Elements (NPEs) and specialized loop controllers.
• Purpose: The paper contextualizes and synthesizes previously reported findings to offer a coherent architectural perspective for students and practitioners, focusing on architectural trade-offs rather than new experimental results.

Technical Details

• Initial Architecture: Based on flexible arrays of tiny RISC-V processing cores operating in an event-driven manner for fully connected networks.
• Interconnect: Communication between cores is managed by a simple Network-on-Chip (NoC).
• Acceleration Strategy: Acceleration is achieved by evolving the architecture to include dedicated Neural Processing Elements (NPEs).
• Control Mechanism: A loop controller is introduced to offload fine-grained control tasks, minimizing the load on the general-purpose RISC-V cores.
• Software Mapping Techniques Discussed:
  • Spike grouping.
  • Event-driven depth-first convolution for processing Convolutional Networks (CNNs).
  • Hard-attention style processing suitable for high-resolution event-based vision systems.
• Key Design Considerations: The analysis focuses heavily on architectural trade-offs, managing performance and energy bottlenecks, and capitalizing on the inherent benefits of sparse activation and event-driven computation.

Implications

• RISC-V Ecosystem Advancement: This tutorial validates RISC-V as a highly adaptable core for building complex, heterogeneous System-on-Chips (SoCs). Using RISC-V cores as the flexible foundation allows for easy integration and control of specialized neuromorphic accelerators (NPEs).
• Democratization of Neuromorphic Design: By providing a structured, step-by-step guide based on a real-world platform (SENECA), the paper lowers the barrier to entry for designing high-performance digital neuromorphic processors, fostering broader innovation in hardware architecture.
• EdgeAI Efficiency: The demonstrated architecture specifically tackles the critical need for power efficiency in EdgeAI. The shift from GPP control to dedicated NPEs and controllers ensures that computation is optimally aligned with the sparse, event-driven nature of neuromorphic workloads, leading to significant energy savings.
• Future Heterogeneous Design: The methodology champions a hybrid approach—combining programmable RISC-V cores for flexibility with dedicated hardware for acceleration—which is likely to become the standard blueprint for future high-performance, domain-specific computing platforms.