Low Power Low Phase Noise 60 GHz Multichannel Transceiver in 28 nm CMOS for Radar Applications

Rimmelspacher, J.; Ciocoveanu, Radu; Steffan, Giovanni; Bassi, Matteo; Issakov, Vadim

doi:10.1109/rfic49505.2020.9218297

Cited by 33 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…𝐸 𝑐 = 𝑁 spikes × 𝐸 spikes + 𝛿𝑇 × 𝑃 leakage (6) where 𝐸 𝑐 is the energy consumed per classification, 𝑁 spikes is the maximum number of spikes during classification, 𝐸 spikes = 2.1 pJ is the energy per spike, 𝑃 leakage = 73 µW is the static leakage power and 𝛿𝑇 is the inference time. Assuming the 𝛿𝑇 to be 28 ms, the energy consumption per classification of the proposed system is 𝐸 𝑐 = 2.05 µJ.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, contactless non-vision-based systems, such as radarbased systems, garnered a lot of attention because of their insensitive nature to the illumination conditions, invariance to hand occlusions, simpler signal processing pipeline, privacy-preserving features, ability to work within an enclosure, and their sensitivity to fine-grained gestures. There are two fundamental research directions to radarbased gesture systems: 1) building efficient miniature hardware for generating high-fidelity target data [4]- [6], and 2) the signal processing pipeline driven by deep learning to extract meaningful information from the target data of the user's intent [7]- [13]. Although the conventional deep neural networks (deepNets) approaches are quite promising in terms of gesture detection and recognition, energy consumption is still an issue making them unfavorable for portable devices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data

2022

Self Cite

View full text Add to dashboard Cite

One of the main challenges in developing embedded radar-based gesture recognition systems is the requirement of energy efficiency. To facilitate this, we present an embedded gesture recognition system using a 60 GHz frequency modulated continuous wave radar using spiking neural networks (SNNs) applied directly to raw ADC data. The SNNs are sparse in time and space, and event-driven which makes them energy-efficient. In contrast to the previous state-of-the-art methods, the proposed system is only based on the raw ADC data of the target thus avoiding the overhead of performing the slow-time and fast-time Fourier transforms (FFTs). Furthermore, the pre-processing slow-time FFT is mimicked in the proposed SNN architecture, where the proposed model processing speed of 12 ms advances the state-of-the-art by a factor of 17.7. The experimental results demonstrate that despite the simplification the proposed implementation achieves recognition accuracy of 98.1%, which is comparable to the conventional approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data

2022

Self Cite

View full text Add to dashboard Cite

show abstract

One-Shot Meta-learning for Radar-Based Gesture Sequences Recognition

Mauro

Chmurski

Arsalan

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Power-efficient gesture sensing for edge devices: mimicking fourier transforms with spiking neural networks

2022

View full text Add to dashboard Cite

One of the key design requirements for any portable/mobile device is low power. To enable such a low powered device, we propose an embedded gesture detection system that uses spiking neural networks (SNNs) applied directly to raw ADC data of a 60GHz frequency modulated continuous wave radar. SNNs can facilitate low power systems because they are sparse in time and space and are event-driven. The proposed system, as opposed to earlier state-of-the-art methods, relies solely on the target’s raw ADC data, thus avoiding the overhead of performing slow-time and fast-time Fourier transforms (FFTs) processing. The proposed architecture mimics the discrete Fourier transformation within the SNN itself avoiding the need for FFT accelerators and makes the FFT processing tailored to the specific application, in this case gesture sensing. The experimental results demonstrate that the proposed system is capable of classifying 8 different gestures with an accuracy of 98.7%. This result is comparable to the conventional approaches, yet it offers lower complexity, lower power consumption and faster computations comparable to the conventional approaches.

show abstract

Low Power Low Phase Noise 60 GHz Multichannel Transceiver in 28 nm CMOS for Radar Applications

Cited by 33 publications

References 1 publication

Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data

Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data

One-Shot Meta-learning for Radar-Based Gesture Sequences Recognition

Power-efficient gesture sensing for edge devices: mimicking fourier transforms with spiking neural networks

Contact Info

Product

Resources

About