Per-Core DVFS with Switched-Capacitor Converters for Energy Efficiency in Manycore Processors

Integrating multiple power converters on-chip improves energy efficiency of manycore architectures. Switched-capacitor (SC) dc-dc converters are compatible with conventional CMOS processes, but traditional implementations suffer from limited conversion efficiency. We propose a dynamic voltage and frequency scaling scheme with SC converters that achieves high converter efficiency by allowing the output voltage to ripple and having the processor core frequency track the ripple. Minimum core energy is achieved by hopping between different converter modes and tuning body-bias voltages. A multicore processor model based on a 28-nm technology shows conversion efficiencies of 90% along with over 25% improvement in the overall chip energy efficiency

A RISC-V Vector Processor With Simultaneous-Switching Switched-Capacitor DC-DC Converters in 28 nm FDSOI

This work demonstrates a RISC-V vector microprocessor implemented in 28 nm FDSOI with fully integrated simultaneous-switching switched-capacitor DC-DC (SC DC-DC) converters and adaptive clocking that generates four on-chip voltages between 0.45 and 1 V using only 1.0 V core and 1.8 V IO voltage inputs. The converters achieve high efficiency at the system level by switching simultaneously to avoid charge-sharing losses and by using an adaptive clock to maximize performance for the resulting voltage ripple. Details about the implementation of the DC-DC switches, DC-DC controller, and adaptive clock are provided, and the sources of conversion loss are analyzed based on measured results. This system pushes the capabilities of dynamic voltage scaling by enabling fast transitions (20 ns), simple packaging (no off-chip passives), low area overhead (16%), high conversion efficiency (80%-86%), and high energy efficiency (26.2 DP GFLOPS/W) for mobile devices

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Dual-Input Switched Capacitor Converter Suitable for Wide Voltage gain Range

International audienceThe capacitive-based switching converter suffers from low efficiency, except for a few conversion ratios, thus limiting its use in fine dynamic voltage and frequency scaling for the power management of digital circuits. Therefore, this paper proposes a Multiple Input Single Output Switched Capacitor Converter (MISO-CSC) to provide flatness efficiency over a large voltage gain range. First, the power efficiency calculation in MISO configuration is given, and then the best ones to optimize the number of switched capacitor structures is selected. By using two power supplies, the MISO converter produces 18 ratios instead of three in SISO (Single Input Single Output) mode. Using a CMOS 65nm technology, the transistor-based simulations exhibit an average 15% efficiency gain over a 0.5-1.4V output voltage range compared to the SISO-CSC. Index Terms— switched capacitor converter, multi-input converter, power efficiency optimization, fully integrated voltage regulator, dynamic voltage and frequency scaling

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

Dynamic Energy Management for Chip Multi-processors under Performance Constraints

We introduce a novel algorithm for dynamic energy management (DEM) under performance constraints in chip multi-processors (CMPs). Using the novel concept of delayed instructions count, performance loss estimations are calculated at the end of each control period for each core. In addition, a Kalman filtering based approach is employed to predict workload in the next control period for which voltage-frequency pairs must be selected. This selection is done with a novel dynamic voltage and frequency scaling (DVFS) algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Using our customized Sniper based CMP system simulation framework, we demonstrate the effectiveness of the proposed algorithm for a variety of benchmarks for 16 core and 64 core network-on-chip based CMP architectures. Simulation results show consistent energy savings across the board. We present our work as an investigation of the tradeoff between the achievable energy reduction via DVFS when predictions are done using the effective Kalman filter for different performance penalty thresholds

An FPGA-based infrastructure for fine-grained DVFS analysis in high-performance embedded systems

Emerging technologies provide SoCs with fine-grained DVFS capabilities both in space (number of domains) and time (transients in the order of tens of nanoseconds). Analyzing these systems requires cycle-accurate accounting of rapidly-changing dynamics and complex interactions among accelerators, interconnect, memory, and OS. We present an FPGA-based infrastructure that facilitates such analyses for high-performance embedded systems. We show how our infrastructure can be used to first generate SoCs with loosely-coupled accelerators, and then perform design-space exploration considering several DVFS policies under full-system workload scenarios, sweeping spatial and temporal domain granularity

Enhancing microprocessor power efficiency through clock-data compensation

The Smartphone revolution and the Internet of Things (IoTs) have triggered rapid advances in complex system-on-chips (SoCs) that increasing provide more functionality within a tight power budget. Highly power efficient on die switched-capacitor voltage regulators suffer from large output voltage ripple preventing their widespread use in modern integrated circuits. With technology scaling and increasing architectural complexity, the number of transistors switching in a power domain is growing rapidly leading to major issues with respect to voltage noise. The large voltage and frequency guard-bands present in current microprocessor designs to combat voltage noise both degrade the performance and erode the energy efficiency of the design. In an effort to reduce guard-bands, adaptive clocking based systems combat the problem of voltage noise by adjusting the clock frequency during a voltage droop to avoid timing failure. This thesis presents an integrated power management and clocking scheme that utilizes clock-data compensation to achieve adaptive clocking. The design is capable of automatically con figuring the supply voltage given a target clock frequency for the load circuit. Furthermore, during a voltage droop the design adjusts clock frequency to meet critical path timing margins while simultaneously increasing the current delivered to the load to recover from the droop. The design was implemented in IBM's 130nm technology and simulation results show that the design is able to clock the load circuit from 30 MHz to 800 Mhz with current efficiencies as high as 97%.M.S

Dynamic Power Management for Neuromorphic Many-Core Systems

This work presents a dynamic power management architecture for neuromorphic many core systems such as SpiNNaker. A fast dynamic voltage and frequency scaling (DVFS) technique is presented which allows the processing elements (PE) to change their supply voltage and clock frequency individually and autonomously within less than 100 ns. This is employed by the neuromorphic simulation software flow, which defines the performance level (PL) of the PE based on the actual workload within each simulation cycle. A test chip in 28 nm SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct PLs. By measurement of three neuromorphic benchmarks it is shown that the total PE power consumption can be reduced by 75%, with 80% baseline power reduction and a 50% reduction of energy per neuron and synapse computation, all while maintaining temporary peak system performance to achieve biological real-time operation of the system. A numerical model of this power management model is derived which allows DVFS architecture exploration for neuromorphics. The proposed technique is to be used for the second generation SpiNNaker neuromorphic many core system