Elastic CGRA Accelerator for Embedded Systems: STRELA

Core Concepts

This work proposes an elastic Coarse-Grained Reconfigurable Architecture (CGRA) integrated into an energy-efficient RISC-V-based System on Chip (SoC) designed for the embedded domain. The microarchitecture supports conditionals and irregular loops, enabling efficient utilization for both simple and complex applications.

Abstract

The paper introduces the STRELA (STReaming ELAstic CGRA) accelerator, which is an elastic CGRA integrated into a RISC-V-based SoC for embedded systems. The key highlights are:

1. Microarchitecture Design:

   • Supports both data-driven and control-driven applications through the addition of comparators, Branch, Merge, and multiplexer modules in the Functional Units (FUs).
   • Uses elastic logic with valid and ready signals to enable latency tolerance and reduce routing complexity.
   • Includes independent memory nodes to decouple address generation from the CGRA core.

2. Mapping Strategies:

   • Supports three mapping strategies: one-shot kernels that fit entirely in the CGRA, one-shot kernels that can be unrolled, and multi-shot kernels that require reconfiguration.
   • Provides guidelines for efficient mapping of applications based on their characteristics.

3. System Integration:

   • Integrates the CGRA accelerator into a RISC-V-based X-HEEP SoC, enabling efficient data streaming between the main memory and the accelerator.
   • Implements power and clock-gating techniques to adapt the architecture to the embedded domain.

The proposed CGRA accelerator is implemented in TSMC 65nm technology and achieves a peak performance of 1.22 GOPs for one-shot kernels and 1.17 GOPs for multi-shot kernels. It also provides significant speed-ups (up to 18.61x) and energy savings (up to 11.10x) compared to the base RISC-V SoC.

Stats

The CGRA accelerator achieves a peak performance of 1.22 GOPs for one-shot kernels and 1.17 GOPs for multi-shot kernels.
The best energy efficiency is 72.68 MOPs/mW for one-shot kernels and 115.96 MOPs/mW for multi-shot kernels.
The best speed-ups are 17.63x and 18.61x for one-shot and multi-shot kernels, respectively.
The best energy savings in the SoC are 9.05x and 11.10x for one-shot and multi-shot kernels, respectively.

Quotes

"This work aims to introduce an elastic Coarse-Grained Reconfigurable Architecture (CGRA) integrated into an energy-efficient RISC-V-based System on Chip (SoC) designed for the embedded domain."
"The microarchitecture of CGRA supports conditionals and irregular loops, making it adaptable to domain-specific applications."
"Due to the integration of CGRA as an accelerator of the RISC-V processor, a versatile and efficient framework is achieved, providing adaptability, processing capacity, and overall performance across a wide range of applications."

Key Insights Distilled From

STRELA: STReaming ELAstic CGRA Accelerator for Embedded Systems

by Daniel Vazqu... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12503.pdf

Deeper Inquiries

How can the mapping strategies be further automated and optimized to improve the utilization of the CGRA resources

To automate and optimize the mapping strategies for better utilization of CGRA resources, several approaches can be considered:

Automated Mapping Tools: Developing specialized tools or compilers that can analyze the application code, extract the DFG, and automatically map it onto the CGRA architecture. These tools can optimize the placement of operations and data flow to maximize resource utilization.

Heuristic Algorithms: Implementing heuristic algorithms that can intelligently partition the application code, determine the optimal placement of operations, and generate efficient mappings. Techniques like graph partitioning, task scheduling, and resource allocation can be employed to improve mapping efficiency.

Performance Modeling: Utilizing performance models to predict the impact of different mapping strategies on CGRA performance. By simulating various mapping scenarios, developers can identify the most effective strategy for a given application and architecture configuration.

Dynamic Reconfiguration: Implementing dynamic reconfiguration mechanisms that can adapt the CGRA configuration on-the-fly based on the changing computational requirements of the application. This dynamic approach can ensure efficient resource utilization at runtime.

Machine Learning: Leveraging machine learning algorithms to analyze past mapping strategies and performance data to predict the most effective mapping for new applications. This data-driven approach can continuously improve mapping efficiency over time.

By combining these approaches and integrating them into the design flow of the CGRA development process, developers can automate and optimize the mapping strategies to enhance resource utilization and overall performance.

What are the potential challenges and trade-offs in extending the CGRA microarchitecture to support floating-point operations and more complex control-flow constructs

Extending the CGRA microarchitecture to support floating-point operations and more complex control-flow constructs can introduce several challenges and trade-offs:

Area and Power Consumption: Adding support for floating-point operations and complex control-flow constructs may increase the area and power consumption of the CGRA. Floating-point units and additional control logic require more hardware resources, impacting the overall efficiency of the accelerator.

Timing Constraints: Implementing complex operations can introduce timing constraints that may affect the overall performance of the CGRA. Ensuring that the design meets timing requirements while supporting floating-point operations and intricate control flows is crucial but challenging.

Resource Allocation: Balancing the allocation of resources for integer and floating-point operations within the CGRA can be a trade-off. Optimizing resource utilization to support both types of operations efficiently without compromising performance is a key challenge.

Programming Complexity: Supporting more complex constructs can increase the programming complexity of the CGRA. Developers need to design efficient algorithms and mapping strategies to effectively utilize the extended microarchitecture capabilities.

Verification and Testing: Extending the microarchitecture introduces new functionalities that require thorough verification and testing to ensure correctness and reliability. Validating the design with floating-point operations and complex control flows adds complexity to the verification process.

By carefully addressing these challenges and trade-offs, designers can successfully extend the CGRA microarchitecture to support floating-point operations and more complex control-flow constructs while maintaining efficiency and performance.

How can the STRELA accelerator be integrated with other heterogeneous components, such as neural network accelerators or domain-specific processors, to create a more comprehensive and versatile embedded system

Integrating the STRELA accelerator with other heterogeneous components, such as neural network accelerators or domain-specific processors, can create a comprehensive and versatile embedded system with enhanced capabilities. Here are some strategies for integration:

Interconnect Design: Designing a flexible and efficient interconnect system to facilitate communication between the STRELA accelerator and other components. Implementing high-bandwidth interfaces and protocols to enable seamless data exchange.

Unified Memory Architecture: Establishing a unified memory architecture that allows different accelerators to access shared memory resources. Implementing memory management techniques to optimize data transfer and minimize latency.

Task Offloading Mechanisms: Developing task offloading mechanisms that enable dynamic allocation of tasks to different accelerators based on workload characteristics. Implementing load balancing algorithms to distribute tasks effectively among the components.

Coherent Processing: Ensuring coherence between the STRELA accelerator and other processors to maintain data consistency and synchronization. Implementing cache coherence protocols and synchronization mechanisms for shared data access.

Software Framework Integration: Integrating the STRELA accelerator with existing software frameworks and development tools to streamline application development and deployment. Providing APIs and libraries for seamless integration with neural network accelerators and domain-specific processors.

By implementing these strategies, the STRELA accelerator can be effectively integrated with other heterogeneous components to create a versatile embedded system capable of handling a wide range of applications efficiently and effectively.