To ensure the correctness and reliability, digital circuits are designed with conservative timing margins to accommodate extreme variations in process, voltage, and temperature (PVT) and workload. However, worst-case scenarios rarely occur, leaving the reserved time margins unutilized, which leads to a waste of performance. This issue is particularly significant in reconfigurable processors, as they exhibit substantial workload timing slack in both spatial and temporal domains. Previous researches have mainly focused on either developing PVT slack or exploiting workload slack, but few have simultaneously considered both aspects. Additionally, directly applying existing timing enhancement techniques to reconfigurable processors is challenging due to their complex configurability and diminishing timing slack in array architectures.To address the above challenges, this paper introduces ACET, an Adaptive Clock scheme which Exploits Timing slack comprehensively through hardware-software co-optimization. On the hardware side, ACET incorporates an adaptive clock module that adjusts the clock period based on both workload and PVT conditions. The two conditions are obtained by employing a PVT delay monitor and encoding the workload-dependent delay into the configuration, respectively. Then timing information is transmitted to phase selection module for cycle-level adjustments, to leverage the temporal timing slack. On the software side, to further exploit the spatial timing slack, a scheduling algorithm is proposed, which heuristically rearranges the firing time of operations. Experiments demonstrate that ACET leads to an average performance increase of 70.1% or an equivalent energy saving of 35.6%, with the hardware overhead being only 0.56%.

ACET: An Adaptive Clock Scheme Exploiting Comprehensive Timing Slack for Reconfigurable Processors

Smart sensing technologies and their inherent data-processing techniques have drawn considerable research and industrial attention in recent years. Recent developments in nanometer CMOS technologies have shown great potential to deal with the increasing demand of processing power that arises in these sensing technologies, from IoT applications to complicated medical devices. Moreover, circuit implementation, which could be based on a full analog or digital approach or, in most cases, on a mixed-signal approach, possesses a fundamental role in exploiting the full capabilities of sensing technologies. In addition, all circuit design methodologies include the optimization of several performance metrics, such as low power, low cost, small area, and high throughput, which impose critical challenges in the field of sensor design. This Special Issue aims to highlight advances in the development, modeling, simulation, and implementation of integrated circuits for sensing technologies, from the component level to complete sensing systems.

/pdf/special-issue-smart-ic-design-and-sensing-technologies-3j78q0gu.pdf

Special Issue “Smart IC Design and Sensing Technologies”

Traditional processor architectures utilize an external DRAM for data storage, while they also operate under worst-case timing constraints. Such designs are heavily constrained by the delay costs of the data transfer between the core pipeline and the DRAM, and they are incapable of exploiting the timing variations of their pipeline stages. In this work, we focus on a near-data processing methodology combined with a novel timing analysis technique that enables the adaptive frequency scaling of the core clock and boosts the performance of low-power designs. We propose a near-data processing and better-than-worst-case co-design methodology to efficiently move the instruction execution to the DRAM side and, at the same time, to allow the pipeline to operate at higher clock frequencies compared to the worst-case approach. To this end, we develop a timing analysis technique, which evaluates the timing requirements of individual instructions and we dynamically scale the clock frequency, according to the instructions types that currently occupy the pipeline. We evaluate the proposed methodology on six different RISC-V post-layout implementations using an HMC DRAM to enable the processing-in-memory (PIM) process. Results indicate an average speedup factor of 1.96× with a 1.6× reduction in energy consumption compared to a standard RISC-V PIM baseline implementation.

Low-power Near-data Instruction Execution Leveraging Opcode-based Timing Analysis

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Low-power Near-data Instruction Execution Leveraging Opcode-based Timing Analysis