15 research outputs found
SoC architecture for acquisition and processing of the EMG signal
2023 38th Conference on Design of Circuits and Integrated Systems (DCIS), 15-17 November 2023, Málaga, Spain.EMG (electromyography) is a technique used to
measure the electrical activity of muscles and nerves, during
contraction and relaxation, and is used in a variety of clinical
and research applications. In particular, it is very useful for
the diagnosis and evaluation of neuromuscular disorders and
control electromechanical devices or prostheses. This signal has a
low SNR and needs to undergo conditioning processes including
amplification, filtering and digitisation for further processing.
In order to improve the SNR of the EMG signals, this work
describes a System-on-Chip (SoC) architecture for the acquisition
and processing of the EMG signals, offering a modular and
high performance solution. These signals could be applied to the
movement of a therapeutic exoskeleton with the aim of improving
active rehabilitation therapies for patients with incomplete spinal
cord injury. The proposed architecture provides a modular
solution that allows signal digitization, performed as close as
possible to the electrode and minimizing transmission losses,
signal noise and artifacts. In addition, sampling is performed at a
higher sampling rate than commercial acquisition systems, while
supporting significant processing throughput. The architecture
uses the ADS1298R integrated circuit for multichannel acquisition
and perform the correct conditioning of EMG signals, as well
as integrate the communication module through Serial Peripheral
Interface (SPI) interface to carry out the configuration and data
transfers. In this case, the acquired signal is processed using a
moving average-based algorithm and its thresholding to establish
the muscle activity. The identified muscle activity could be used
as an active reference to activate the therapeutic exoskeleton. The
results show the validation of the proposed architecture with an
EMG signal with a sampling rate of 8 kSPS.Agencia Estatal de Investigació
Column-row addressing of thermo-optic phase shifters for controlling large silicon photonic circuits
We demonstrate a time-multiplexed row-column addressing scheme to drive thermo-optic phase shifters in a silicon photonic circuit. By integrating a diode in series with the heater, we can connect heaters in an matrix topology to row and column lines. The heaters are digitally driven with pulse-width modulation, and time-multiplexed over different channels. This makes it possible to drive the circuit without digital-to-analog converters, and using only wires. We demonstrate this concept with a power splitter tree with 15 thermo-optic phase shifters that are controlled in a matrix, connected through 8 bond pads. This technique is especially useful in silicon photonic circuits with many tuners but limited space for electrical connections
SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection
This paper proposes SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection. SafeDB provides a comprehensive and systematic hardware-based security framework from the bitstream protection to data confidentiality, especially for the cloud environment. The AES key shared between FPGA and client for the bitstream encryption is generated in hard-wired logic using PKI and ECC. The data security is assured by the enclaved processing with encrypted data, meaning that the encrypted data is processed inside the FPGA fabric. Thus, no one in the system is able to look into clients\u27 data because plaintext data are not exposed to memory and/or memory-mapped space. SafeDB is resistant not only to the side channel attack but to the attacks from malicious insiders. We have constructed an 8-node cluster prototype with Zynq UltraScale+ FPGAs to demonstrate the security, performance, and practicability
Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels
Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embedded computer vision (e.g. multi-core CPUs, GPUs, and FPGAs), and their associated vendor optimized vision libraries, it becomes a challenge for developers to navigate this fragmented solution space. To aid with determining which embedded platform is most suitable for their application, we conduct a comprehensive benchmark of the run-time performance and energy efficiency of a wide range of vision kernels. We discuss rationales for why a given underlying hardware architecture innately performs well or poorly based on the characteristics of a range of vision kernel categories. Specifically, our study is performed for three commonly used HW accelerators for embedded vision applications: ARM57 CPU, Jetson TX2 GPU and ZCU102 FPGA, using their vendor optimized vision libraries: OpenCV, VisionWorks and xfOpenCV. Our results show that the GPU achieves an energy/frame reduction ratio of 1.1–3.2× compared to the others for simple kernels. While for more complicated kernels and complete vision pipelines, the FPGA outperforms the others with energy/frame reduction ratios of 1.2–22.3×. It is also observed that the FPGA performs increasingly better as a vision application’s pipeline complexity grows
Programmable photonics : an opportunity for an accessible large-volume PIC ecosystem
We look at the opportunities presented by the new concepts of generic programmable photonic integrated circuits (PIC) to deploy photonics on a larger scale. Programmable PICs consist of waveguide meshes of tunable couplers and phase shifters that can be reconfigured in software to define diverse functions and arbitrary connectivity between the input and output ports. Off-the-shelf programmable PICs can dramatically shorten the development time and deployment costs of new photonic products, as they bypass the design-fabrication cycle of a custom PIC. These chips, which actually consist of an entire technology stack of photonics, electronics packaging and software, can potentially be manufactured cheaper and in larger volumes than application-specific PICs. We look into the technology requirements of these generic programmable PICs and discuss the economy of scale. Finally, we make a qualitative analysis of the possible application spaces where generic programmable PICs can play an enabling role, especially to companies who do not have an in-depth background in PIC technology
Analysis of Memory-Contention in Heterogeneous COTS MPSoCs
Multiple-Processors Systems-on-Chip (MPSoCs) provide an appealing platform to execute Mixed Criticality Systems (MCS) with both time-sensitive critical tasks and performance-oriented non-critical tasks. Their heterogeneity with a variety of processing elements can address the conflicting requirements of those tasks. Nonetheless, the complex (and hence hard-to-analyze) architecture of Commercial-Off-The-Shelf (COTS) MPSoCs presents a challenge encumbering their adoption for MCS. In this paper, we propose a framework to analyze the memory contention in COTS MPSoCs and provide safe and tight bounds to the delays suffered by any critical task due to this contention. Unlike existing analyses, our solution is based on two main novel approaches. 1) It conducts a hybrid analysis that blends both request-level and task-level analyses into the same framework. 2) It leverages available knowledge about the types of memory requests of the task under analysis as well as contending tasks; specifically, we consider information that is already obtainable by applying existing static analysis tools to each task in isolation. Thanks to these novel techniques, our comparisons with the state-of-the art approaches show that the proposed analysis provides the tightest bounds across all evaluated access scenarios
Milestones in Autonomous Driving and Intelligent Vehicles Part \uppercase\expandafter{\romannumeral1}: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing
at a rapid pace due to the convenience, safety, and economic benefits. Although
a number of surveys have reviewed research achievements in this field, they are
still limited in specific tasks and lack systematic summaries and research
directions in the future. Our work is divided into 3 independent articles and
the first part is a Survey of Surveys (SoS) for total technologies of AD and
IVs that involves the history, summarizes the milestones, and provides the
perspectives, ethics, and future research directions. This is the second part
(Part \uppercase\expandafter{\romannumeral1} for this technical survey) to
review the development of control, computing system design, communication, High
Definition map (HD map), testing, and human behaviors in IVs. In addition, the
third part (Part \uppercase\expandafter{\romannumeral2} for this technical
survey) is to review the perception and planning sections. The objective of
this paper is to involve all the sections of AD, summarize the latest technical
milestones, and guide abecedarians to quickly understand the development of AD
and IVs. Combining the SoS and Part \uppercase\expandafter{\romannumeral2}, we
anticipate that this work will bring novel and diverse insights to researchers
and abecedarians, and serve as a bridge between past and future.Comment: 18 pages, 4 figures, 3 table
Hardware Accelerated DNA Sequencing
DNA sequencing technology is quickly evolving. The latest developments ex-
ploit nanopore sensing and microelectronics to realize real-time, hand-held devices.
A critical limitation in these portable sequencing machines is the requirement of
powerful data processing consoles, a need incompatible with portability and wide
deployment. This thesis proposes a rst step towards addressing this problem, the
construction of specialized computing modules { hardware accelerators { that can
execute the required computations in real-time, within a small footprint, and at a
fraction of the power needed by conventional computers. Such a hardware accel-
erator, in FPGA form, is introduced and optimized specically for the basecalling
function of the DNA sequencing pipeline. Key basecalling computations are identi-
ed and ported to custom FPGA hardware. Remaining basecalling operations are
maintained in a traditional CPU which maintains constant communications with
its FPGA accelerator over the PCIe bus. Measured results demonstrated a 137X
basecalling speed improvement over CPU-only methods while consuming 17X less
power than a CPU-only method