FPGA
FPGA
It wouldn't be feasible because FPGAs aren't designed for general-purpose computing. They're specialized hardware meant for specific tasks, not for running complex operating systems or dual-booting multiple OSes like Windows and macOS on ARM. Additionally, companies rely on these CPUs for revenue, so changing them would disrupt business models. There isn’t a fundamental scientific barrier, but practical and economic factors make it unworkable.
There are CPUs that include FPGAs. MacOS supports both X64 and Arm64 architectures. You don’t necessarily need an FPGA to achieve this. Originally, CPUs were essentially FPGAs when they first appeared, but they became more refined during production. Manufacturers added extra instruction sets to simplify programming and optimization. It’s not worth the effort to create a custom FPGA when standard CPUs already offer better performance at lower costs.
The adaptability of FPGA increases the expense significantly, both in terms of cost and transistor count. Consider an FPGA as a set of LEGO bricks—while you can assemble various structures, the limited variety restricts complexity and raises the price compared to a dedicated processor. A well-designed processor uses highly specialized and optimized components, with layouts that minimize signal travel distances within the silicon. For instance, achieving 5 GHz signals often requires keeping them under 4 mm inside the die. In contrast, a FPGA might span 50 mm by 50 mm, but this size demands careful planning to ensure signals stay within the allowed path.
It's challenging to match today's CPU performance with an FPGA in the same speed range. Gaining flexibility often means sacrificing efficiency. You can still use a dedicated FPGA module for particular tasks, which is increasingly common in data centers and scientific applications. These modules usually cost as much as several computers combined. The idea of simply reprogramming them for new functions remains a long-term goal, likely taking years or decades to become practical. Developing the necessary infrastructure for OSes and programs to dynamically switch components is complex, and the skilled professionals needed are scarce and expensive.
The more detailed you get about a component, the better it performs in that area but less so elsewhere. Starting with a CPU gives us broad capabilities, though not always fast. Moving to a GPU focuses on speed for specific tasks, sacrificing versatility. If it doesn’t fit those roles, its performance drops significantly. FPGAs offer flexibility by being programmed for particular functions, making them efficient in their niche but less adaptable overall. Custom ASICs excel at single tasks with exceptional precision.