A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

Absorbed Dose Uncertainty Estimation for Proton Therapy

Successful radiotherapy treatment depends on the absorbed dose evaluation and the possibility to define metrological characteristics of the therapy beam. Radiotherapy requires tumor dose delivery with expanded uncertainty less than +/- 5 %. It is particularly important to reduce uncertainty during therapy beam calibration as well as to apply all necessary ionization chamber correction factors. Absorbed dose to water was determined using ionometric method. Calibration was performed in reference cobalt beam. Combined standard uncertainty of the calculated absorbed dose to water in 65 MeV proton beam was +/- 1.97% while the obtained expanded uncertainty of absorbed dose for the same beam quality was +/- 5.02%. The uncertainty estimation method has been developed within the project TESLA

Monte Carlo Calculation of the Energy Response Characteristics of a RadFET Radiation Detector

The Metal -Oxide Semiconductor Field-Effect-Transistor (MOSFET, RadFET) is frequently used as a sensor of ionizing radiation in nuclear-medicine, diagnostic-radiology, radiotherapy quality-assurance and in the nuclear and space industries. We focused our investigations on calculating the energy response of a p-type RadFET to low-energy photons in range from 12 keV to 2 MeV and on understanding the influence of uncertainties in the composition and geometry of the device in calculating the energy response function. All results were normalized to unit air kerma incident on the RadFET for incident photon energy of 1.1 MeV. The calculations of the energy response characteristics of a RadFET radiation detector were performed via Monte Carlo simulations using the MCNPX code and for a limited number of incident photon energies the FOTELP code was also used for the sake of comparison. The geometry of the RadFET was modeled as a simple stack of appropriate materials. Our goal was to obtain results with statistical uncertainties better than 1% (fulfilled in MCNPX calculations for all incident energies which resulted in simulations with 1 - 2x10(9) histories.13th IMEKO TC1-TC7 Joint Symposium Without Measurement No Science, Without Science No Measurement, Sep 01-03, 2010, City Univ London, London, Englan

BinaryEye: A 20 kfps Streaming Camera System on FPGA with Real-Time On-Device Image Recognition Using Binary Neural Networks

Streaming high-speed cameras pose a major challenge to distributed cyber-physical and IoT systems, because large data volumes need to be transferred under stringent realtime constraints. Edge processing can mitigate the data deluge by extracting relevant information from image data on-device with low latency. This work presents an FPGA-based 20 kfps streaming camera system, which can classify regions of interest (ROI) within a frame with a binarized neural network (BNN) in realtime streaming mode, achieving massive data reduction. BNNs have the potential to enable energy-efficient image classifications for on-device processing. We demonstrate our system in a case study with a simple real-time BNN classifier achieving 19.28 us latency at 0.52 W power consumption and resulting in a 980x data reduction. We compare external image processing with this result, showing 3x energy savings, and discuss the used HDL/HLS design flow for BNN implementation

Self-sustainable smart ring for long-term monitoring of blood oxygenation

Medical devices measure vital parameters such as pulse, respiration rate, and blood oxygenation, over periods of days or weeks in a continuous manner. Traditional systems only support such requirements in stationary applications where a constant power supply is available. Trends toward remote healthcare and telemedicine require wearable devices, able to provide similar functionalities in wireless mode. Miniaturized and thin form factors, desirable in wearable applications, set stringent constraints on the available power, and consequently on the accuracy and lifetime. Energy harvesting combined with low-power design and energy efficient processing can significantly extend the lifetime of wearable devices. This paper presents a wearable pulse oximeter assembled in a 3D ring-like geometry that achieves self-sustainability by exploiting efficient power management, solar energy harvesting, and ultra-low power processing in a multi-core microcontroller. The design strategy of combining onboard processing to monitor blood oxygenation and the transmission of only relevant information via a Bluetooth low-energy (BLE) interface, significantly reduces the overall energy consumption. Experimental results on the designed and developed prototype demonstrate that measuring the blood oxygenation once every minute with a sampling rate of 100 samples/s achieve accurate results at the daily energy consumption of 28 J including hourly BLE transmissions. The low-power design allows the system to be self-sustainable with just 64 min of sunlight per day or 12 hrs. of indoor home light

Self-Sustainable Smart Ring for Long-Term Monitoring of Blood Oxygenation

Medical devices measure vital parameters such as pulse, respiration rate, and blood oxygenation, over periods of days or weeks in a continuous manner. Traditional systems only support such requirements in stationary applications where a constant power supply is available. Trends toward remote healthcare and telemedicine require wearable devices, able to provide similar functionalities in wireless mode. Miniaturized and thin form factors, desirable in wearable applications, set stringent constraints on the available power, and consequently on the accuracy and lifetime. Energy harvesting combined with low-power design and energy efficient processing can significantly extend the lifetime of wearable devices. This paper presents a wearable pulse oximeter assembled in a 3D ring-like geometry that achieves self-sustainability by exploiting efficient power management, solar energy harvesting, and ultra-low power processing in a multi-core microcontroller. The design strategy of combining onboard processing to monitor blood oxygenation and the transmission of only relevant information via a Bluetooth low-energy (BLE) interface, significantly reduces the overall energy consumption. Experimental results on the designed and developed prototype demonstrate that measuring the blood oxygenation once every minute with a sampling rate of 100 samples/s achieve accurate results at the daily energy consumption of 28 J including hourly BLE transmissions. The low-power design allows the system to be self-sustainable with just 64 min of sunlight per day or 12 hrs. of indoor home light.ISSN:2169-353

Self-Sustainable Smart Ring for Long-Term Monitoring of Blood Oxygenation

Medical devices measure vital parameters such as pulse, respiration rate, and blood oxygenation, over periods of days or weeks in a continuous manner. Traditional systems only support such requirements in stationary applications where a constant power supply is available. Trends toward remote healthcare and telemedicine require wearable devices, able to provide similar functionalities in wireless mode. Miniaturized and thin form factors, desirable in wearable applications, set stringent constraints on the available power, and consequently on the accuracy and lifetime. Energy harvesting combined with low-power design and energy efficient processing can significantly extend the lifetime of wearable devices. This paper presents a wearable pulse oximeter assembled in a 3D ring-like geometry that achieves self-sustainability by exploiting efficient power management, solar energy harvesting, and ultra-low power processing in a multi-core microcontroller. The design strategy of combining onboard processing to monitor blood oxygenation and the transmission of only relevant information via a Bluetooth low-energy (BLE) interface, significantly reduces the overall energy consumption. Experimental results on the designed and developed prototype demonstrate that measuring the blood oxygenation once every minute with a sampling rate of 100 samples/s achieve accurate results at the daily energy consumption of 28 J including hourly BLE transmissions. The low-power design allows the system to be self-sustainable with just 64 min of sunlight per day or 12 hrs. of indoor home light