arXiv subjects

Mohamed Shahawy

Publications and source records attributed to Mohamed Shahawy.

3 recordsLinked to original sources

arXiv2025

Bombyx: OpenCilk Compilation for FPGA Hardware Acceleration

Task-level parallelism (TLP) is a widely used approach in software where independent tasks are dynamically created and scheduled at runtime. Recent systems have explored architectural support for TLP on field-programmable gate arrays (FPGAs), often leveraging high-level synthesis (HLS) to create processing elements (PEs). In this paper, we present Bombyx, a compiler toolchain that lowers OpenCilk programs into a Cilk-1-inspired intermediate representation, enabling efficient mapping of CPU-oriented TLP applications to spatial architectures on FPGAs. Unlike OpenCilk's implicit task model, which requires costly context switching in hardware, Cilk-1 adopts explicit continuation-passing - a model that better aligns with the streaming nature of FPGAs. Bombyx supports multiple compilation targets: one is an OpenCilk-compatible runtime for executing Cilk-1-style code using the OpenCilk backend, and another is a synthesizable PE generator designed for HLS tools like Vitis HLS. Additionally, we introduce a decoupled access-execute optimization that enables automatic generation of high-performance PEs, improving memory-compute overlap and overall throughput.

cs.AR↗

arXiv2023

HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization

The traditional Neural Network-development process requires substantial expert knowledge and relies heavily on intuition and trial-and-error. Neural Architecture Search (NAS) frameworks were introduced to robustly search for network topologies, as well as facilitate the automated development of Neural Networks. While some optimization approaches -- such as Genetic Algorithms -- have been extensively explored in the NAS context, other Metaheuristic Optimization algorithms have not yet been investigated. In this study, we evaluate the viability of Artificial Bee Colony optimization for Neural Architecture Search. Our proposed framework, HiveNAS, outperforms existing state-of-the-art Swarm Intelligence-based NAS frameworks in a fraction of the time.

cs.NE↗

arXiv2023

Exploring the Intersection between Neural Architecture Search and Continual Learning

Despite the significant advances achieved in Artificial Neural Networks (ANNs), their design process remains notoriously tedious, depending primarily on intuition, experience and trial-and-error. This human-dependent process is often time-consuming and prone to errors. Furthermore, the models are generally bound to their training contexts, with no considerations to their surrounding environments. Continual adaptiveness and automation of neural networks is of paramount importance to several domains where model accessibility is limited after deployment (e.g IoT devices, self-driving vehicles, etc.). Additionally, even accessible models require frequent maintenance post-deployment to overcome issues such as Concept/Data Drift, which can be cumbersome and restrictive. By leveraging and combining approaches from Neural Architecture Search (NAS) and Continual Learning (CL), more robust and adaptive agents can be developed. This study conducts the first extensive review on the intersection between NAS and CL, formalizing the prospective Continually-Adaptive Neural Networks (CANNs) paradigm and outlining research directions for lifelong autonomous ANNs.

cs.AI↗