Power Profiling Model for RISC-V Core

The reduction of power consumption is considered to be a critical factor for efficient computation of microprocessors. Therefore, it is necessary to implement a power management system that is aware of the computational load of the CPU cores. To enable such power management, this project aims to develop a power profiling model for the RISC-V core. TheSyDeKick verification environment was used to develop the power profiling models. Additionally, Python-controlled mixed mode simulations of C-programs compiled for A-Core were conducted to obtain needed data for the power profiling of the digital circuitry. The proposed methodology could employ a time-varying power consumption profiling for the A-Core RISC-V microprocessor core which depends on software, voltage, and clock frequency. The results of this project allow for the creation of parameterized power profiles for the A-Core, which can contribute to more efficient and sustainable computing

Multi-core devices for safety-critical systems: a survey

Multi-core devices are envisioned to support the development of next-generation safety-critical systems, enabling the on-chip integration of functions of different criticality. This integration provides multiple system-level potential benefits such as cost, size, power, and weight reduction. However, safety certification becomes a challenge and several fundamental safety technical requirements must be addressed, such as temporal and spatial independence, reliability, and diagnostic coverage. This survey provides a categorization and overview at different device abstraction levels (nanoscale, component, and device) of selected key research contributions that support the compliance with these fundamental safety requirements.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-65316-P, Basque Government under grant KK-2019-00035 and the HiPEAC Network of Excellence. The Spanish Ministry of Economy and Competitiveness has also partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft

Efficient and Scalable Computing for Resource-Constrained Cyber-Physical Systems: A Layered Approach

With the evolution of computing and communication technology, cyber-physical systems such as self-driving cars, unmanned aerial vehicles, and mobile cognitive robots are achieving increasing levels of multifunctionality and miniaturization, enabling them to execute versatile tasks in a resource-constrained environment. Therefore, the computing systems that power these resource-constrained cyber-physical systems (RCCPSs) have to achieve high efficiency and scalability. First of all, given a fixed amount of onboard energy, these computing systems should not only be power-efficient but also exhibit sufficiently high performance to gracefully handle complex algorithms for learning-based perception and AI-driven decision-making. Meanwhile, scalability requires that the current computing system and its components can be extended both horizontally, with more resources, and vertically, with emerging advanced technology. To achieve efficient and scalable computing systems in RCCPSs, my research broadly investigates a set of techniques and solutions via a bottom-up layered approach. This layered approach leverages the characteristics of each system layer (e.g., the circuit, architecture, and operating system layers) and their interactions to discover and explore the optimal system tradeoffs among performance, efficiency, and scalability. At the circuit layer, we investigate the benefits of novel power delivery and management schemes enabled by integrated voltage regulators (IVRs). Then, between the circuit and microarchitecture/architecture layers, we present a voltage-stacked power delivery system that offers best-in-class power delivery efficiency for many-core systems. After this, using Graphics Processing Units (GPUs) as a case study, we develop a real-time resource scheduling framework at the architecture and operating system layers for heterogeneous computing platforms with guaranteed task deadlines. Finally, fast dynamic voltage and frequency scaling (DVFS) based power management across the circuit, architecture, and operating system layers is studied through a learning-based hierarchical power management strategy for multi-/many-core systems

A RISC-V Processor SoC With Integrated Power Management at Submicrosecond Timescales in 28 nm FD-SOI

46th European Solid-State Circuits Conference (ESSCIRC), Lausanne, SWITZERLAND, SEP 13-15, 2016International audienceThis paper presents a RISC-V system-on-chip (SoC) with integrated voltage regulation, adaptive clocking, and power management implemented in a 28 nm fully depleted silicon-on-insulator process. A fully integrated simultaneous-switching switched-capacitor DC-DC converter supplies an application core using a clock from a free-running adaptive clock generator, achieving high system conversion efficiency (82%-89%) and energy efficiency (41.8 double-precision GFLOPS/W) while delivering up to 231 mW of power. A second core serves as an integrated power-management unit that can measure system state and actuate changes to core voltage and frequency, allowing the implementation of a wide variety of power-management algorithms that can respond at submicrosecond timescales while adding just 2.0% area overhead. A voltage dithering program allows operation across a wide continuous voltage range (0.45 V-1 V), while an adaptive voltage-scaling algorithm reduces the energy consumption of a synthetic benchmark by 39.8% with negligible performance penalty, demonstrating practical microsecond-scale power management for mobile SoCs