Hardware vs. software implementation trade-offs: Evaluate the statement — “A hardware solution is always faster than a software solution for a digital system design.”

Introduction / Context:
Choosing between hardware and software implementations depends on performance, latency, flexibility, power, cost, and development time. While custom hardware can exploit parallelism and dedicated pipelines, it is not universally faster than software running on optimized processors with caches, SIMD, and accelerators.

Given Data / Assumptions:

• “Hardware” may refer to ASICs, FPGAs, or dedicated coprocessors.
• “Software” may run on microcontrollers, CPUs, DSPs, or GPUs.
• Performance depends on algorithm structure and available parallelism.

Concept / Approach:
Speed is task-dependent. Hardware wins when abundant fine-grained parallelism exists and latency must be minimal. However, modern software platforms can be faster for control-dominated tasks, irregular data structures, or when using high-performance GPUs. Overheads such as I/O, memory bandwidth, and clock constraints also limit hardware throughput.

Step-by-Step Solution:

Verification / Alternative check:
Benchmarks show some signal-processing kernels excel on FPGAs, while others run faster on GPUs or CPUs due to clock speeds, caches, and massive parallel cores. Control-oriented tasks often favor software.

Why Other Options Are Wrong:

• Correct: Not correct because speed depends on the task and platform.
• True only for DSP / above 200 MHz / instruction width only: Each provides an oversimplified rule that ignores memory, I/O, and algorithmic structure.

Common Pitfalls:
Overlooking data movement costs; ignoring development time and maintainability; assuming clock frequency directly equals performance.

Final Answer:
Incorrect