This paper presents an enhanced Ant Colony Optimization (ACO) algorithm tailored for optimizing CMOS physical design in VLSI circuits. As device complexity escalates, traditional placement techniques struggle with multiobjective goals such as minimizing layout area, wirelength, and achieving effective aspect ratios. The proposed ACO framework simulates artificial ant colonies exploring layout configurations and reinforcing promising solutions through a pheromone-guided heuristic. Evaluated on a benchmark containing ten typical logic blocks—Adder, Multiplier, Shifter, MUX, Register, ALU, Decoder, Control, Cache, and Buffer—the ACO method achieves a maximum layout area reduction of 27.27% (from 1760 to 1280 units²) and improves the aspect ratio from 3.64 to 5.0 compared to traditional layouts. The mean area reduction observed across different parameter settings is approximately 20%. The system also includes a fully configurable and modular automation tool designed for flexible parameter tuning and the rapid benchmarking of the ACO algorithm. This tool enables users to easily adjust key parameters such as number of ants, iteration count, pheromone evaporation rate, and heuristic influences, allowing for a comprehensive exploration of the optimization space. Experimental results demonstrate ACO’s scalability, adaptability, and effectiveness, establishing it as a viable approach for automation in complex physical designs. Future work will focus on hybrid algorithms and multi-objective optimization extensions.

This paper evaluates HPC-style CPU performance and cost in virtualized cloud infrastructures using a subset of OpenMP workloads in the SPEC ACCEL suite. Four major cloud providers by market share AWS, Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI) are compared across Intel, AMD, and ARM general purpose instance types under both on-demand and one-year discounted pricing. AWS consistently delivers the shortest runtime in all three instance types, yet charges a premium, especially for on-demand usage. OCI emerges as the most economical option across all CPU families, although it generally runs workloads more slowly than AWS. Azure often exhibits mid-range performance and cost, while GCP presents a mixed profile: it sees a notable boost when moving from Intel to AMD. On the other hand, its ARM instance is more than twice as slow as its own AMD offering and remains significantly more expensive. AWS's internal comparisons reveal that its ARM instance can outperform its Intel and AMD siblings by up to 49 percent in runtime. These findings highlight how instance choices and provider selection can yield substantial variations in both runtime and price, indicating that workload priorities, whether raw speed or cost minimization, should guide decisions on instance types.

The growing complexity of computational workloads has amplified the need for efficient and specialized hardware accelerators. Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) have emerged as prominent solutions, each excelling in specific domains. Although there is substantial research comparing FPGAs and GPUs, most of the work focuses primarily on performance metrics, offering limited insight into the specific types of applications that each accelerator benefits the most. This paper aims to bridge this gap by synthesizing insights from various research articles to guide users in selecting the appropriate accelerator for domain-specific applications. By categorizing the reviewed studies and analyzing key performance metrics, this work highlights the strengths, limitations, and ideal use cases for FPGAs and GPUs. The findings offer actionable recommendations, helping researchers and practitioners navigate trade-offs in performance, energy efficiency, and programmability.

Cloud computing has become the cornerstone of modern IT infrastructure, offering a wide range of general-purpose instances optimized for diverse workloads. This paper provides a comparative analysis of cost and performance for general-purpose compute instances across four major cloud providers: AWS, Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI). Using standardized configurations of 4 vCPUs and 16 GiB of RAM, the study evaluates instances based on processor architecture (Intel, AMD, ARM), pricing models, and performance benchmarks. Key findings reveal that ARM-based instances deliver superior price-performance ratios for cost-sensitive workloads, while Intel-based instances excel in enterprise-grade applications requiring versatility and reliability. The results aim to guide organizations in selecting the most cost-effective and performance-efficient cloud resources for their specific needs.