CF
C. Frenkel
17 records found
1
Traditional computing approaches based on the von Neumann architecture consist of physically separate storage and computation units. This requires the data to be moved back and forth between the storage and computation units, resulting in increased latency and energy costs known
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Radar-based sensors are used to perceive their environment and objects of interest in a contactless manner and with robust performance in all weather and light conditions. One of the main drawbacks is the energy needed for the processing of radar data in order to extract its valu
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State-space models (SSMs) combine attention-like parallelization with RNN-like inference efficiency, using internal states with linear update and output functions, similar to RNNs but without non-linearities in the update function. Linear Recurrent Units (LRUs), a type of SSM, ar
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This report serves to finalize the bachelor graduation project on the topic of self-supervised federated learning, specifically the implementation of the algorithms in Python. The goal of the project is to implement a self-supervised learning setup in a decentralized approach usi
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Self-Supervised Federated Learning at the Edge
Hardware & System Development
This thesis serves to finalise the bachelor graduation project on the topic of self-supervised federated learning, specifically the on-chip implementation of the algorithms. The goal of the project is to implement a self-supervised learning setup in a decentralised approach using
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Novel Neuromorphic Hardware Inspired by the Olfactory Pathway Model of the Drosophila
Leveraging bio-plausible computational primitives in digital circuits for spatio-temporal processing
Olfactory learning in Drosophila larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and a
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Nowadays, to reduce the dependence of devices on cloud servers, machine learning workloads are required to process data on the edge. Furthermore, to improve adaptability to uncontrolled environments, there is a growing need for on-chip learning. Limitations in power and area for
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Event-based cameras promise new opportunities for smart vision systems deployed at the edge. Contrary to their conventional frame-based counterparts, event-based cameras generate temporal light intensity changes as events on a per-pixel basis, enabling ultra-low latency with micr
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The growing interest in edge computing is driving the demand for more efficient deep learning models that fit into resource-constrained edge devices like Internet-of-Things (IoT) sensors. The challenging limitations of these devices in terms of size and power has given rise to th
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Spiking neural networks (SNNs), which are regarded as the third generation of neural networks, have attracted significant attention due to their promising applications in various scenarios. Based on SNNs, neuromorphic coprocessors, designed to emulate the structure and functional
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High-speed asynchronous digital interfaces
Exploiting the spatiotemporal correlations of event-based sensor data
With the introduction of event-based cameras, such as the dynamic vision sensor (DVS), new opportunities have arisen for low-latency real-time visual data processing. Unlike traditional frame-based cameras that capture entire frames at fixed intervals, each pixel in an event-base
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AI on Low-Cost Hardware
FPGA subgroup
In the past decades, much progress has been made in the field of AI, and now many different algorithms exist that reach very high accuracies. Unfortunately, many of these algorithms are quite resource intensive, which makes them unavailable on low-cost devices.
The aim of th ...
The aim of th ...
AI on low-cost hardware
Microcontroller subgroup
The creation of effective computational models that function within the power limitations of edge de- vices is an important research problem in the field of Artificial Intelligence (AI). While cutting-edge deep learning algorithms show promising results, they frequently need comp
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AI on Low-Cost Hardware
Software Subgroup
Artificial Intelligence has become a dominant part of our lives, however, complex artificial intelligence models tend to use a lot of energy, computationally complex operations, and a lot of memory resources. Therefore, it excluded a whole class of hardware in its applicability.
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Recent trends in machine learning (ML) have placed a strong emphasis on power- and resource-efficient neural networks, as well as the development of neural networks on edge devices. Spiking neural net-works (SNNs), due to their event-based nature, are one of the most promising ty
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Voice activity detection (VAD) is the prevailing approach to extracting meaningful speech information from the pervasive noise found in the physical environment. Presently, deep neural networks (DNN) are widely employed as the classifier component in Voice Activity Detection (VAD
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