5,141 research outputs found
A software controlled voltage tuning system using multi-purpose ring oscillators
This paper presents a novel software driven voltage tuning method that
utilises multi-purpose Ring Oscillators (ROs) to provide process variation and
environment sensitive energy reductions. The proposed technique enables voltage
tuning based on the observed frequency of the ROs, taken as a representation of
the device speed and used to estimate a safe minimum operating voltage at a
given core frequency. A conservative linear relationship between RO frequency
and silicon speed is used to approximate the critical path of the processor.
Using a multi-purpose RO not specifically implemented for critical path
characterisation is a unique approach to voltage tuning. The parameters
governing the relationship between RO and silicon speed are obtained through
the testing of a sample of processors from different wafer regions. These
parameters can then be used on all devices of that model. The tuning method and
software control framework is demonstrated on a sample of XMOS XS1-U8A-64
embedded microprocessors, yielding a dynamic power saving of up to 25% with no
performance reduction and no negative impact on the real-time constraints of
the embedded software running on the processor
A Scalable Correlator Architecture Based on Modular FPGA Hardware, Reuseable Gateware, and Data Packetization
A new generation of radio telescopes is achieving unprecedented levels of
sensitivity and resolution, as well as increased agility and field-of-view, by
employing high-performance digital signal processing hardware to phase and
correlate large numbers of antennas. The computational demands of these imaging
systems scale in proportion to BMN^2, where B is the signal bandwidth, M is the
number of independent beams, and N is the number of antennas. The
specifications of many new arrays lead to demands in excess of tens of PetaOps
per second.
To meet this challenge, we have developed a general purpose correlator
architecture using standard 10-Gbit Ethernet switches to pass data between
flexible hardware modules containing Field Programmable Gate Array (FPGA)
chips. These chips are programmed using open-source signal processing libraries
we have developed to be flexible, scalable, and chip-independent. This work
reduces the time and cost of implementing a wide range of signal processing
systems, with correlators foremost among them,and facilitates upgrading to new
generations of processing technology. We present several correlator
deployments, including a 16-antenna, 200-MHz bandwidth, 4-bit, full Stokes
parameter application deployed on the Precision Array for Probing the Epoch of
Reionization.Comment: Accepted to Publications of the Astronomy Society of the Pacific. 31
pages. v2: corrected typo, v3: corrected Fig. 1
SpinLink: An interconnection system for the SpiNNaker biologically inspired multi-computer
SpiNNaker is a large-scale biologically-inspired multi-computer designed to model very heavily distributed problems, with the flagship application being the simulation of large neural networks. The project goal is to have one million processors included in a single machine, which consequently span many thousands of circuit boards. A computer of this scale imposes large communication requirements between these boards, and requires an extensible method of connecting to external equipment such as sensors, actuators and visualisation systems. This paper describes two systems that can address each of these problems.Firstly, SpinLink is a proposed method of connecting the SpiNNaker boards by using time-division multiplexing (TDM) to allow eight SpiNNaker links to run at maximum bandwidth between two boards. SpinLink will be deployed on Spartan-6 FPGAs and uses a locally generated clock that can be paused while the asynchronous links from SpiNNaker are sending data, thus ensuring a fast and glitch-free response. Secondly, SpiNNterceptor is a separate system, currently in the early stages of design, that will build upon SpinLink to address the important external I/O issues faced by SpiNNaker. Specifically, spare resources in the FPGAs will be used to implement the debugging and I/O interfacing features of SpiNNterceptor
Improving reconfigurable systems reliability by combining periodical test and redundancy techniques: a case study
This paper revises and introduces to the field of reconfigurable computer systems, some traditional techniques used in the fields of fault-tolerance and testing of digital circuits. The target area is that of on-board spacecraft electronics, as this class of application is a good candidate for the use of reconfigurable computing technology. Fault tolerant strategies are used in order for the system to adapt itself to the severe conditions found in space. In addition, the paper describes some problems and possible solutions for the use of reconfigurable components, based on programmable logic, in space applications
Platform for Testing and Evaluation of PUF and TRNG Implementations in FPGAs
Implementation of cryptographic primitives like
Physical Unclonable Functions (PUFs) and True Random Number
Generators (TRNGs) depends significantly on the underlying
hardware. Common evaluation boards offered by FPGA vendors
are not suitable for a fair benchmarking, since they have different
vendor dependent configuration and contain noisy switching
power supplies. The proposed hardware platform is primary
aimed at testing and evaluation of cryptographic primitives
across different FPGA and ASIC families. The modular platform
consists of a motherboard and exchangeable daughter board
modules. These are designed to be as simple as possible to
allow cheap and independent evaluation of cryptographic blocks
and namely PUFs. The motherboard is based on the Microsemi
SmartFusion 2 SoC FPGA. It features a low-noise power supply,
which simplifies evaluation of vulnerability to the side channel
attacks. It provides also means of communication between the
PC and the daughter module. Available software tools can be
easily customized, for example to collect data from the random
number generator located in the daughter module and to read it
via USB interface. The daughter module can be plugged into
the motherboard or connected using an HDMI cable to be
placed inside a Faraday cage or a temperature control chamber.
The whole platform was designed and optimized to fullfil the
European HECTOR project (H2020) requirements
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
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