Accelerator Design Patterns — Comparison Matrix

Comparison matrix

Accelerators differ in dataflow, locality assumptions, and integration cost.

+------------------+----------------+----------------+----------------+
| Approach         | Strength       | Weakness       | Best when      |
+------------------+----------------+----------------+----------------+
| SIMD             | regular loops  | divergence     | media          |
| Systolic         | matmul         | fixed          | ML GEMM        |
| Dataflow         | utilization    | programming    | inference      |
| GPU-style        | flexible       | overhead       | general offload |
+------------------+----------------+----------------+----------------+

When to choose each approach

• Pick baseline when schedule risk dominates

• Pick structural only with proof

Interview traps

• One approach always best

• Ignoring software burden

Architecture deep dive

Accelerators win on locality and bandwidth contracts, not peak OPS alone.

Concept diagram

ACCELERATOR DATAFLOW

Host CPU ── commands ──► Queue / scheduler
   ▲                         │
   │ completion              ▼
Coherent memory ◄── DMA ── Local SRAM ──► Compute array
                         ▲       │
                         └ tiles ┘

Peak TOPS matters only when data reaches the array at the needed rate.

Metric graph

UTILIZATION BREAKDOWN

compute active   ██████████████████  58%
DMA wait         ██████████          31%
host sync        █████               15%
cache/coherency  ████                12%
idle bubbles     ███████             22%

Low utilization is usually a system integration problem.

Metrics and artifacts

• accelerator utilization

• DMA bandwidth

• kernel launch overhead

• coherency invalidation rate

Mini case study

NPU met TOPs target but end-to-end inference slow — DMA and weight fetch dominated. Architecture added on-chip SRAM tile and double-buffering.

Debug branches

• If util low, check launch overhead and host sync first.

• If BW high, examine weight layout and sparsity support.

Senior review question

Ask: what single metric would prove this concept is working or failing on your workload?

Key takeaways

• Connect every architecture claim to a workload and measurable metric.

• State verification and PPA impact before proposing design changes.

Common pitfalls

• Feature-driven design without MPKI/IPC/bandwidth evidence.

• Ignoring coherency and NoC traffic in cache and accelerator sizing.

Study notes

Re-read this topic with one concrete workload.