Common Mistakes in FPGA ML Accelerator Design
Common Pitfalls in FPGA ML Accelerator Design (and How to Avoid Them)
External Article ↗
This article was originally published at Vicharak.
It outlines common design mistakes encountered while building FPGA-based ML accelerators, along with practical approaches to avoid them.
Why this matters
Most FPGA ML designs fail not because of algorithms, but because of system-level issues—memory bandwidth, dataflow design, and poor hardware-software partitioning.
My context
These insights come from building real accelerator pipelines, where theoretical efficiency often breaks under constraints like DRAM latency, limited on-chip memory, and data movement overhead.
What’s not obvious at first
Compute is rarely the bottleneck. Data movement dominates. Designs that ignore this end up underutilizing hardware despite having “correct” implementations.