Related papers: APEX: Automated Parameter Exploration for Low-Powe…
In this paper an optimal relay selection algorithm with non-negligible probing time is proposed and analyzed for cooperative wireless networks. Relay selection has been introduced to solve the degraded bandwidth efficiency problem in…
The performance analysis of wireless CSMA networks is notoriously difficult due to the intricate sensing and interference relationships among links. Even the fundamental problem of throughput characterization remains open when sensing and…
This paper investigates the optimization of the long-standing probabilistically robust transmit beamforming problem with channel uncertainties in the multiuser multiple-input single-output (MISO) downlink transmission. This problem poses…
LoRaWAN is an emerging Low-Power Wide Area Network (LPWAN) technology, which is gaining momentum thanks to its flexibility and ease of deployment. Conversely to other LPWAN solutions, LoRaWAN indeed permits the configuration of several…
This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging…
Today, many network applications require shorter react time. Robotic field is an excellent example of these needs: robot react time has a direct effect on its task's complexity. Here, we propose a full deterministic medium access method for…
We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and quantization policy, we optimize them in a joint manner. To…
As wireless researchers are tasked to enable wireless communication as infrastructure in more dynamic aerial settings, there is a growing need for large-scale experimental platforms that provide realistic, reproducible, and reliable…
Compressed sensing is a promising technique that attempts to faithfully recover sparse signal with as few linear and nonadaptive measurements as possible. Its performance is largely determined by the characteristic of sensing matrix.…
Neuromorphic neural network processors, in the form of compute-in-memory crossbar arrays of memristors, or in the form of subthreshold analog and mixed-signal ASICs, promise enormous advantages in compute density and energy efficiency for…
In contemporary machine learning workloads, numerous hyper-parameter search algorithms are frequently utilized to efficiently discover high-performing hyper-parameter values, such as learning and regularization rates. As a result, a range…
With the rising complexity of numerous novel applications that serve our modern society comes the strong need to design efficient computing platforms. Designing efficient hardware is, however, a complex multi-objective problem that deals…
Efficient management of heterogeneous traffic with varying priorities is critical in Wireless Body Area Networks (WBANs). The priority mechanisms embedded in Media Access Control (MAC) schemes largely govern the performance of WBAN in terms…
Hardware-Aware Neural Architecture Search (HW-NAS) is an efficient approach to automatically co-optimizing neural network performance and hardware energy efficiency, making it particularly useful for the development of Deep Neural Network…
A key strategy for making production in factories more efficient is to collect data about the functioning of machines, and dynamically adapt their working. Such smart factories have data packets with a mix of stringent and non-stringent…
Probe Requests are Wi-Fi management frames periodically sent by devices during network discovery. Tracking Probe Requests over time offers insights into movement patterns, traffic flows, and behavior trends, which are keys in applications…
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most…
While variable selection is essential to optimize the learning complexity by prioritizing features, automating the selection process is preferred since it requires laborious efforts with intensive analysis otherwise. However, it is not an…
There is a clear need for efficient algorithms to tune hyperparameters for statistical learning schemes, since the commonly applied search methods (such as grid search with N-fold cross-validation) are inefficient and/or approximate.…
To better understand complexity in neural networks, we theoretically investigate the idealised phenomenon of lossless network compressibility, whereby an identical function can be implemented with fewer hidden units. In the setting of…