High Level comparative analysis of CPU, GPU, FPGA and ASICS technologies

This comparative analysis explores various silicon computational technologies, including GPUs, FPGAs, ASICs, and CPUs. Each technology is evaluated based on its advantages, disadvantages, key applications, computational power range, and price range. GPUs excel in parallel processing, making them ideal for graphics and AI tasks, but they come with high power consumption and cost. FPGAs offer high customization and low latency, suitable for real-time processing, though they are complex to program. ASICs provide optimized performance for specific tasks with low power consumption but involve high development costs. CPUs, known for their versatility and general-purpose use, balance performance and cost but lack the parallel processing power of GPUs and FPGAs. This analysis highlights the unique strengths and limitations of each technology, guiding their application in various computational scenarios.

The Venn diagram illustrates the overlapping applications of three types of computing hardware: CPUs (Central Processing Units), GPUs (Graphics Processing Units), and FPGAs (Field-Programmable Gate Arrays).

CPU (Central Processing Unit)

• Applications: General-purpose applications, databases, high-performance computing (HPC), web services, and business applications.
• Characteristics: CPUs are versatile and capable of handling a wide range of tasks, making them suitable for general-purpose computing needs.

GPU (Graphics Processing Unit)

• Applications: Domain-specific applications, artificial intelligence (AI), and gaming.
• Characteristics: GPUs excel in parallel processing, making them ideal for tasks that require high computational power, such as AI and graphics rendering.

FPGA (Field-Programmable Gate Array)

• Applications: Data-intensive applications.
• Characteristics: FPGAs are highly customizable and can be configured to perform specific tasks efficiently, making them suitable for applications that require low latency and high throughput.

Overlapping Areas

• CPU and FPGA: Big data analytics, where the combination of general-purpose processing and customizable hardware can handle large datasets effectively.
• All Three (CPU, GPU, FPGA): Real-time applications and search functions, which benefit from the combined strengths of general-purpose processing, parallel computation, and data-intensive capabilities.

This diagram highlights how different types of computing hardware can be leveraged for specific applications, and how their capabilities intersect to address complex computational tasks.