Computer architecture: adding which component is specifically intended to reduce processing time by offloading specialized computation from the main CPU?

Introduction / Context:
System performance improves when work is parallelized or delegated to hardware tailored for specific tasks. A co-processor is extra processing hardware—such as a floating-point unit, graphics processor, or AI accelerator—that executes certain operations faster or more efficiently than the general-purpose CPU, thereby reducing total processing time for workloads heavy in those operations.

Given Data / Assumptions:

• We are comparing common hardware terms.
• Goal: reduce processing time via computation offload.
• Examples include math co-processors, GPUs, DSPs, and NPUs.

Concept / Approach:
A co-processor accelerates specific instruction classes (e.g., floating-point math, vector operations, graphics pipelines). By handling these computations concurrently or more efficiently, the system completes tasks faster. Other listed components—buffers, EPROMs, converters—serve different roles (smoothing I/O, non-volatile storage, analog/digital conversion) and are not compute accelerators per se.

Step-by-Step Solution:
Match each option to its primary function.Identify the option whose primary purpose is computation acceleration.Select “A co-processor.”

Verification / Alternative check:
Benchmarks routinely show speedups when workloads are offloaded to GPUs or dedicated FPUs, confirming the time-reduction role of co-processors.

Why Other Options Are Wrong:
A buffer smooths data flow; EPROM provides read-only storage; a converter changes signal domains. None directly reduce compute time the way a co-processor does.

Common Pitfalls:
Assuming more memory or buffering alone equals faster compute; without computational acceleration, CPU-bound tasks may see little benefit.

Final Answer:
A co-processor