RISC-V R-Extension: Advancing Efficiency with Rented-Pipeline for Edge DNN Processing
Abstract
This paper introduces the RISC-V R-extension, a novel architectural approach designed to enhance Deep Neural Network (DNN) processing efficiency on lightweight edge devices. The R-extension avoids the high power, cost, and area requirements of traditional NPUs by employing custom instructions and specialized hardware features. Key technical innovations include rented-pipeline stages and Architectural Pipeline Registers (APR) that optimize critical operation execution, thereby significantly reducing latency and memory access frequency.
Report
Structured Report: RISC-V R-Extension
Key Highlights
- The primary goal is to address the inefficiency, high power consumption, high cost, and large area overhead of traditional Network Processing Units (NPUs) in lightweight edge devices.
- The solution is the RISC-V R-extension, leveraging the modularity of the RISC-V Instruction Set Architecture (ISA).
- The extension introduces a novel mechanism referred to as the rented-pipeline stage.
- This architecture utilizes Architectural Pipeline Registers (APR) to optimize critical operation execution.
- The R-extension is shown to boost processing efficiency for DNN inference on edge devices, setting the stage for more responsive applications.
Technical Details
| Feature | Description | Purpose |
|---|---|---|
| Architecture | RISC-V R-extension | Enhancing base RISC-V architecture for AI acceleration. |
| Core Mechanism | Rented-pipeline stages | Optimized pipeline usage for frequent, critical operations, avoiding full NPU integration. |
| Hardware Component | Architectural Pipeline Registers (APR) | Registers dedicated to buffering data within the pipeline, minimizing external memory access. |
| Software Interface | New Custom Instructions | Instructions developed specifically to interface with and utilize the rented-pipeline and APR components. |
| Performance Impact | Reduced latency and memory access frequency | Achieved through internal pipeline optimization and register utilization. |
Implications
- Validation of RISC-V Extensibility: This work further demonstrates the power and flexibility of the RISC-V ISA, showing that highly specialized accelerators for tasks like DNN inference can be seamlessly integrated as standard extensions without massive architectural overhaul.
- Lowering the Barrier for Edge AI: By providing a low-cost, low-area, and power-efficient alternative to full dedicated NPUs, the R-extension makes high-performance DNN acceleration feasible for the most constrained, lightweight edge devices.
- Future of Modular Acceleration: The concept of 'renting' pipeline stages suggests a model where general-purpose CPUs can temporarily incorporate specialized execution units (or stages) for specific heavy workloads, improving performance while maintaining area efficiency and modularity across the compute ecosystem.
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