48,859 research outputs found
Operational Amplifier Design in CMOS at Low-Voltage for Sensor Input Front-End Circuits in VLSI Devices
Today, digital circuit cores provide the main circuit implementation approach for integrated circuit (IC) functions in very-large-scale integration (VLSI) circuits and systems. Typical functions include sensor signal input, data storage, digital signal processing (DSP) operations, system control and communications. Despite the fact that a large portion of the circuitry may be developed and implemented using digital logic techniques, there is still a need for high performance analogue circuits such as amplifiers and filters that provide signal conditioning functionality prior to sampling into the digital domain using an analogue-to-digital converter (ADC) for analogue sensor signals. The demands on the design require a multitude of requirements to be taken into account. In this chapter, the design of the operational amplifier (op-amp) is discussed as an important circuit within the front-end circuitry of a mixed-signal IC. The discussion will focus on the design of the op-amp using different compensation schemes incorporating negative Miller compensation and designed to operate at lower power supply voltage levels. A design case study is included which utilises the g
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ratio design approach to determine the transistor sizes. The simulation approach is focussed on the open-loop frequency response performance of the op-amp
PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform
Computing with high-dimensional (HD) vectors, also referred to as
, is a brain-inspired alternative to computing with
scalars. Key properties of HD computing include a well-defined set of
arithmetic operations on hypervectors, generality, scalability, robustness,
fast learning, and ubiquitous parallel operations. HD computing is about
manipulating and comparing large patterns-binary hypervectors with 10,000
dimensions-making its efficient realization on minimalistic ultra-low-power
platforms challenging. This paper describes HD computing's acceleration and its
optimization of memory accesses and operations on a silicon prototype of the
PULPv3 4-core platform (1.5mm, 2mW), surpassing the state-of-the-art
classification accuracy (on average 92.4%) with simultaneous 3.7
end-to-end speed-up and 2 energy saving compared to its single-core
execution. We further explore the scalability of our accelerator by increasing
the number of inputs and classification window on a new generation of the PULP
architecture featuring bit-manipulation instruction extensions and larger
number of 8 cores. These together enable a near ideal speed-up of 18.4
compared to the single-core PULPv3
Space-division multiplexing for fiber-wireless communications
We envision the application of optical Space-division Multiplexing (SDM) to
the next generation fiber-wireless communications as a firm candidate to
increase the end user capacity and provide adaptive radiofrequency-photonic
interfaces. This approach relies on the concept of fiber-distributed signal
processing, where the SDM fiber provides not only radio access distribution but
also broadband microwave photonics signal processing. In particular, we present
two different SDM fiber technologies: dispersion-engineered heterogeneous
multicore fiber links and multicavity devices built upon the selective
inscription of gratings in homogeneous multicore fibers.Comment: 4 pages, 20th International Conference on Transparent Optical
Networks (ICTON), Girona (Spain), 2017. arXiv admin note: text overlap with
arXiv:1810.1213
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
DSPSR: Digital Signal Processing Software for Pulsar Astronomy
DSPSR is a high-performance, open-source, object-oriented, digital signal
processing software library and application suite for use in radio pulsar
astronomy. Written primarily in C++, the library implements an extensive range
of modular algorithms that can optionally exploit both multiple-core processors
and general-purpose graphics processing units. After over a decade of research
and development, DSPSR is now stable and in widespread use in the community.
This paper presents a detailed description of its functionality, justification
of major design decisions, analysis of phase-coherent dispersion removal
algorithms, and demonstration of performance on some contemporary
microprocessor architectures.Comment: 15 pages, 10 figures, to be published in PAS
ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints
Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio
Benchmarking CPUs and GPUs on embedded platforms for software receiver usage
Smartphones containing multi-core central processing units (CPUs) and powerful many-core graphics processing units (GPUs) bring supercomputing technology into your pocket (or into our embedded devices). This can be exploited to produce power-efficient, customized receivers with flexible correlation schemes and more advanced positioning techniques. For example, promising techniques such as the Direct Position Estimation paradigm or usage of tracking solutions based on particle filtering, seem to be very appealing in challenging environments but are likewise computationally quite demanding. This article sheds some light onto recent embedded processor developments, benchmarks Fast Fourier Transform (FFT) and correlation algorithms on representative embedded platforms and relates the results to the use in GNSS software radios. The use of embedded CPUs for signal tracking seems to be straight forward, but more research is required to fully achieve the nominal peak performance of an embedded GPU for FFT computation. Also the electrical power consumption is measured in certain load levels.Peer ReviewedPostprint (published version
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