A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices (1608.08376v1)

Abstract: Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities, from a few kOPS to GOPS. Near-threshold(NT) operation can achieve higher energy efficiency, and the performance scalability can be gained through parallelism. In this paper we describe the design of an open-source RISC-V processor core specifically designed for NT operation in tightly coupled multi-core clusters. We introduce instruction-extensions and microarchitectural optimizations to increase the computational density and to minimize the pressure towards the shared memory hierarchy. For typical data-intensive sensor processing workloads the proposed core is on average 3.5x faster and 3.2x more energy-efficient, thanks to a smart L0 buffer to reduce cache access contentions and support for compressed instructions. SIMD extensions, such as dot-products, and a built-in L0 storage further reduce the shared memory accesses by 8x reducing contentions by 3.2x. With four NT-optimized cores, the cluster is operational from 0.6V to 1.2V achieving a peak efficiency of 67MOPS/mW in a low-cost 65nm bulk CMOS technology. In a low power 28nm FDSOI process a peak efficiency of 193MOPS/mW(40MHz, 1mW) can be achieved.

• The paper demonstrates a novel near-threshold RISC-V core with DSP extensions that achieves a 3.5× speedup and 3.2× energy efficiency improvement, reaching up to 193 MOPS/mW.
• It employs microarchitectural optimizations such as an L0 prefetch buffer and compressed instruction handling to mitigate memory bottlenecks and enhance throughput.
• Benchmarking against ARM Cortex-M series confirms its scalable performance for ultra-low-power, data-intensive sensor processing in IoT applications.

Essay on "A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices"

The paper presents an in-depth examination of a novel RISC-V processor core enhanced with digital signal processing (DSP) extensions, specifically aimed at near-threshold (NT) operation to cater to the ultra-low power requirements of Internet-of-Things (IoT) endpoint devices. The motivation stems from the ever-increasing demand for energy-efficient, scalable processing units that can function within the stringent power budgets typical of IoT applications.

The core has been meticulously designed to combine RISC-V’s open-instruction set architecture (ISA) with custom extensions to optimize energy efficiency without compromising on computational throughput. The authors focus keenly on microarchitectural enhancements that increase computational density while minimizing shared memory contention, which is a critical bottleneck in many-core systems.

Key Contributions and Technical Insights

1. Microarchitectural Optimizations: The proposed design incorporates an L0 buffer with prefetch capability to alleviate the bandwidth pressure on the instruction memory hierarchy. Notably, the buffer caters to compressed instructions, enhancing code density and system throughput.
2. DSP Extensions: By incorporating Single Instruction Multiple Data (SIMD) extensions such as dot-products and a shuffle instruction, the core drastically reduces the load/store operations necessary for data manipulation, thereby enhancing computational efficiency significantly.
3. Fixed-Point and Saturated Arithmetic: The execution stage has been optimized to support fixed-point arithmetic with saturation and normalization capabilities, which are crucial for a wide range of signal processing tasks in resource-constrained environments.
4. Energy Efficiency: Achieving a peak energy efficiency of 193 MOPS/mW in a low-power 28nm FDSOI process underlines the effectiveness of the proposed optimizations. The paper highlights a 3.5× speedup and a 3.2× improvement in energy efficiency for typical data-intensive sensor processing workloads compared to baseline configurations.
5. Comparative Performance Evaluation: By benchmarking against existing RISC architectures and proprietary solutions like ARM Cortex-M series, the authors provide compelling evidence of the RISC-V core’s competitive performance, particularly noteworthy in vector processing and fixed-point arithmetic-intensive applications.

Numerical Results and Bold Claims

The enhanced core exhibits impressive numerical results, achieving energy efficiencies of 67 MOPS/mW and 193 MOPS/mW under different technological implementations (65nm and 28nm). The authors claim that the proposed ISA extensions can significantly align the performance of general-purpose cores with that of specialized hardware accelerators, reducing the traditional performance gap by factors of tenfold.

Theoretical and Practical Implications

The paper embarks on tackling the primary challenges encountered in low-power multi-core designs—most prominently, the memory hierarchy and interconnectivity. The introduction of a shared memory architecture utilizing tightly-coupled data memories (TCDMs) and efficient cache management strategies provides a scalable solution for parallel execution within confined power envelopes. The research underscores the importance of an open-source ISA like RISC-V in catalyzing freedom for application-specific optimizations without tethering to proprietary systems.

Future Trajectories

Looking forward, the adaptability of RISC-V with such comprehensive extensions opens avenues for a range of IoT applications, potentially extending into domains requiring real-time data processing with strict latency requirements. The scalability presented by the PULP platform allows for easy integration of more cores, effectively meeting varied computational demands across spectrum applications.

In conclusion, the paper effectively demonstrates the potential of open-source architectures enhanced with domain-specific extensions to outperform traditional cores in ultra-low-power environments. With continued advancements in near-threshold operation technologies, coupled with architectural innovations, IoT endpoint devices can achieve unprecedented efficiency levels, heralding a new era of smart, scalable hardware solutions.