Related papers: Reconfiguration Strategies for Online Hardware Mul…

Motivated by deep neural network applications, we study the problem of scheduling splittable jobs (e.g., neural network inference tasks) on configurable machines (e.g., multi-instance GPUs). We are given $n$ jobs and a set $C$ of…

In the context of embedded systems design, two important challenges are still under investigation. First, improve real-time data processing, reconfigurability, scalability, and self-adjusting capabilities of hardware components. Second,…

Coarse-grained reconfigurable architectures aim to achieve both goals of high performance and flexibility. However, existing reconfigurable array architectures require many resources without considering the specific application domain.…

We proposes a platform which can generate hardware/software description based on flexible in-struction set architectures (ISAs). The platform takes advantage of the flexibility of field pro-grammable gate array (FPGA) to design many micro…

Management of communication by on-line routing in new FPGAs with a large amount of logic resources and partial reconfigurability is a new challenging problem. A Network-on-Chip (NoC) typically uses packet routing mechanism, which has often…

General-purpose processors feature a limited number of instructions based on an instruction set. They can be numerous, such as with vector extensions that include hundreds or thousands of instructions, but this comes at a cost; they are…

Digital Signal Processing functions are widely used in real time high speed applications. Those functions are generally implemented either on ASICs with inflexibility, or on FPGAs with bottlenecks of relatively smaller utilization factor or…

In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-based heterogeneous platforms, aligning with the principles of sustainable computing. Instead of…

Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs…

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data…

Floorplanning problem has been extensively explored for homogeneous FPGAs. Most modern FPGAs consist of heterogeneous resources in the form of configurable logic blocks, DSP blocks, BRAMs and more. Very little work has been done for…

Radio Resource Management is a challenging topic in future 6G networks where novel applications create strong competition among the users for the available resources. In this work we consider the frequency scheduling problem in a multi-user…

The edge computing paradigm has emerged to handle cloud computing issues such as scalability, security and low response time among others. This new computing trend heavily relies on ubiquitous embedded systems on the edge. Performance and…

Faster, cheaper, and more power efficient optimization solvers than those currently offered by general-purpose solutions are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We…

By exploiting the superiority of non-orthogonal multiple access (NOMA), NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of…

We describe algorithmic results for two crucial aspects of allocating resources on computational hardware devices with partial reconfigurability. By using methods from the field of computational geometry, we derive a method that allows…

In recent years, utilization of heterogeneous hardware other than small core CPU such as GPU, FPGA or many core CPU is increasing. However, when using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL are…

Reconfigurable optical topologies are emerging as a promising technology to improve the efficiency of datacenter networks. This paper considers the problem of scheduling opportunistic links in such reconfigurable datacenters. We study the…

Intrusion detection systems (IDS) are crucial security measures nowadays to enforce network security. Their task is to detect anomalies in network communication and identify, if not thwart, possibly malicious behavior. Recently, machine…

There is a need for machine learning models to evolve in unsupervised circumstances. New classifications may be introduced, unexpected faults may occur, or the initial dataset may be small compared to the data-points presented to the system…