Related papers: Challenges and Opportunities in Implementing Negat…
Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill…
In this paper, we demonstrate by simulation the general usability of an electrostatically doped and electrically reconfigurable planar field-effect transistor (FET) structure. The device concept is partly based on our already published and…
Tunnel Field Effect Transistors (TFET) have extremely low leakage current, exhibit excellent subthreshold swing and are less susceptible to short channel effects. However, TFETs do face certain special challenges, particularly with respect…
Negative feedback is a powerful approach capable of improving several aspects of a system. In linear electronics, it has been critical for allowing invariance to device properties. Negative feedback is also known to enhance linearity in…
Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solution, Vaswani et al. introduced the Transformer, a model…
Development of modern integrated circuit technologies makes it feasible to develop cheaper, faster and smaller special purpose signal processing function circuits. Digital Signal processing functions are generally implemented either on…
The evolution toward sixth-generation (6G) wireless networks demands high-performance transceiver architectures capable of handling complex and dynamic environments. Conventional orthogonal frequency-division multiplexing (OFDM) receivers…
Reconfigurable reflectarray antennas (RRAs) have rapidly developed with various prototypes proposed in recent literatures. However, designing wideband, multiband, or high-frequency RRAs faces great challenges, especially the lengthy…
The high tech industry which requires fast stable motion with nanometer precision continues to mainly use PID which is limited by fundamental linear control limitations. Floor vibrations as disturbance significantly affect performance and…
This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling Real-time Rendering of NeRFs across Devices. Re-ReND is…
Often, the storage and computational constraints of embeddeddevices demand that a single on-device ASR model serve multiple use-cases / domains. In this paper, we propose aFlexibleTransducer(FlexiT) for on-device automatic speech…
Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities while still having run-time constraints. As a result, there…
Metal-based electronics is attractive for fast and radiation-hard electronic circuits and remains one of the longstanding goals for researchers. The emergence of 1T-TaS$_2$, a layered material exhibiting strong charge density wave (CDW)…
Electrical Resistivity Tomography (ERT) has become widely used for engineering and environmental applications in the last couple of decades due to (1) the simplification and automating of resistivity meters and (2) the new generation of…
The paper discusses an application of the field effected transistor (FET) as a voltage controlled resistance which can be done in under-graduate labs either as a routine experiment or as a project.
This work reports flexible fully transparent high-voltage diodes that feature high rectification ratio (Rr 10 8) and high breakdown voltage (Vb 150 V) simultaneously, combined with their applications as building blocks of energy management…
Large-scale integration of emerging nanoscale non-volatile memory devices, e.g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems. Such…
Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest in executing RNNs on embedded devices. However, difficulties…
Transformers are the de-facto choice for sequence modelling, yet their quadratic self-attention and weak temporal bias can make long-range forecasting both expensive and brittle. We introduce FreezeTST, a lightweight hybrid that interleaves…
We propose an analytical device model for a graphene nanoribbon field-effect transistor (GNR-FET). The GNR-FET under consideration is based on a heterostructure which consists of an array of nanoribbons clad between the highly conducting…