CPU, Analog, FPGA, or ASIC?

Algorithms can be implemented across a wide spectrum of hardware, each with its own trade-offs in speed, power, flexibility, cost, and scalability. Let’s compare the four main approaches:

1. Software on General-Purpose CPUs

Pros:

• Easy to develop and debug: Rich toolchains, IDEs, and profiling tools.
• Highly flexible: Reprogram anytime; modify algorithms at will.
• Low development cost: No custom hardware needed; ready to run on PCs, servers, or microcontrollers.
• Ecosystem and libraries: Access to optimized math libraries (e.g., FFTW, NumPy, BLAS).

Cons:

• Latency and real-time constraints: OS overhead and unpredictable timing make hard real-time difficult. Soft real-time is achievable.
• Performance limitations: Limited parallelism compared to hardware solutions.
• High power consumption per operation: Especially inefficient for repetitive, simple tasks.

Ideal for general-purpose applications.

2. Analog Circuits

Pros:

• Ultra-low latency: Signal is processed in real-time with no sampling delay.
• Potentially high throughput: Continuous operation with no clock constraints.
• Minimal power: No digital switching, especially useful in low-power sensors or RF front-ends.
• No need for ADC/DAC: Processes raw analog signals directly.

Cons:

• Limited precision: Susceptible to noise, drift, and component tolerances.
• Hard to scale: Each additional function requires more physical components.
• Difficult to tune or reconfigure: Redesign often requires physical changes.
• No programmability: Once built, behavior is fixed or only marginally tunable.

Ideal for real-time sensing, analog filters, RF circuits, ultra-low power embedded front-ends.

3. Field-Programmable Gate Arrays (FPGAs)

Pros:

• High parallelism: True concurrent execution of multiple operations.
• Low deterministic latency: Ideal for real-time pipelines.
• Reconfigurable hardware: Algorithms can be updated post-deployment.
• Power-efficient: Much better performance-per-watt than CPUs for many tasks.

Cons:

• Steep learning curve: Requires HDL knowledge (VHDL/Verilog) or high-level synthesis.
• Toolchain complexity: Longer compile/synthesis times, debugging can be difficult.
• Moderate development cost: More expensive than CPUs in small volumes.
• Not optimal for floating-point math: Often better with fixed-point arithmetic.

Ideal for real-time video/audio processing, signal processing, robotics, hardware prototyping.

4. Custom Chips (ASICs)

Pros:

• Maximum performance: Custom datapaths, memory layouts, and logic yield unmatched throughput.
• Lowest power consumption: Fully optimized for the task at hand.
• Smallest footprint: No unnecessary hardware or software overhead.
• Production cost scales well: Extremely cheap per unit at high volumes.

Cons:

• Astronomically high NRE (non-recurring engineering) cost: Millions of dollars just to reach first silicon.
• Long time-to-market: Can take 6–24 months from design to tapeout.
• Zero flexibility: Bugs in logic mean hardware re-spins.
• High risk: A single design flaw can cost months of work and millions in losses.

Ideal for high-volume commercial products (e.g., smartphones, wireless chips), aerospace, medical devices, deep learning accelerators. Example: u-blox