2,853 research outputs found
Low Power Processor Architectures and Contemporary Techniques for Power Optimization â A Review
The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER
Optimal Memoryless Encoding for Low Power Off-Chip Data Buses
Off-chip buses account for a significant portion of the total system power
consumed in embedded systems. Bus encoding schemes have been proposed to
minimize power dissipation, but none has been demonstrated to be optimal with
respect to any measure. In this paper, we give the first provably optimal and
explicit (polynomial-time constructible) families of memoryless codes for
minimizing bit transitions in off-chip buses. Our results imply that having
access to a clock does not make a memoryless encoding scheme that minimizes bit
transitions more powerful.Comment: Proceedings of the 2006 IEEE/ACM international Conference on
Computer-Aided Design (San Jose, California, November 05 - 09, 2006). ICCAD
'06. ACM, New York, NY, 369-37
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
A 100-MIPS GaAs asynchronous microprocessor
The authors describe how they ported an asynchronous microprocessor previously implemented in CMOS to gallium arsenide, using a technology-independent asynchronous design technique. They introduce new circuits including a sense-amplifier, a completion detection circuit, and a general circuit structure for operators specified by production rules. The authors used and tested these circuits in a variety of designs
Approximate energy-efficient encoding for serial interfaces
Serial buses are ubiquitous interconnections in embedded computing systems that are used to interface processing elements with peripherals, such as sensors, actuators, and I/O controllers. Despite their limited wiring, as off-chip connections they can account for a significant amount of the total power consumption of a system-on-chip device. Encoding the information sent on these buses is the most intuitive and affordable way to reduce their power contribution; moreover, the encoding can be made even more effective by exploiting the fact that many embedded applications can tolerate intermediate approximations without a significant impact on the final quality of results, thus trading off accuracy for power consumption. We propose a simple yet very effective approximate encoding for reducing dynamic energy in serial buses. Our approach uses differential encoding as a baseline scheme and extends it with bounded approximations to overcome the intrinsic limitations of differential encoding for data with low temporal correlation. We show that the proposed scheme, in addition to yielding extremely compact codecs, is superior to all state-of-the-art approximate serial encodings over a wide set of traces representing data received or sent from/to sensor or actuators
Modeling and Analysis of Noise and Interconnects for On-Chip Communication Link Design
This thesis considers modeling and analysis of noise and interconnects in onchip communication. Besides transistor count and speed, the capabilities of a modern design are often limited by on-chip communication links. These links typically consist of multiple interconnects that run parallel to each other for long distances between functional or memory blocks. Due to the scaling of technology, the interconnects have considerable electrical parasitics that affect their performance, power dissipation and signal integrity. Furthermore, because of electromagnetic coupling, the interconnects in the link need to be considered as an interacting group instead of as isolated signal paths. There is a need for accurate and computationally effective models in the early stages of the chip design process to assess or optimize issues affecting these interconnects. For this purpose, a set of analytical models is developed for on-chip data links in this thesis.
First, a model is proposed for modeling crosstalk and intersymbol interference. The model takes into account the effects of inductance, initial states and bit sequences. Intersymbol interference is shown to affect crosstalk voltage and propagation delay depending on bus throughput and the amount of inductance. Next, a model is proposed for the switching current of a coupled bus. The model is combined with an existing model to evaluate power supply noise. The model is then applied to reduce both functional crosstalk and power supply noise caused by a bus as a trade-off with time. The proposed reduction method is shown to be effective in reducing long-range crosstalk noise.
The effects of process variation on encoded signaling are then modeled. In encoded signaling, the input signals to a bus are encoded using additional signaling circuitry. The proposed model includes variation in both the signaling circuitry and in the wires to calculate the total delay variation of a bus. The model is applied to study level-encoded dual-rail and 1-of-4 signaling.
In addition to regular voltage-mode and encoded voltage-mode signaling, current-mode signaling is a promising technique for global communication. A model for energy dissipation in RLC current-mode signaling is proposed in the thesis. The energy is derived separately for the driver, wire and receiver termination.Siirretty Doriast
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