Sounds like a great plan, but the devil’s in the details. Sparse matrices can slash FLOPs, but they also mess with memory bandwidth and scheduling. You’ll need a tile‑based scheduler that can pack non‑zeros evenly; otherwise, the compute units sit idle while the DMA pulls the rest. Think of a simple 8‑bit quantized kernel—if you use a compressed sparse column format, you can shave off a lot of power, but the decoder logic gets a bit ugly. Maybe start with a small 4×4 block, profile the sparsity, and then expand. Also, don’t forget that the energy per multiply‑accumulate is lower, but the extra address logic can dominate if you’re not careful. A little tweaking of the pruning threshold could get you that sweet spot between latency and energy. Give it a shot, but keep a log of the fill‑rate per cycle—those numbers are the real performance verdict.

Nice plan, just remember that a 4×4 tile still needs a decent depth‑first fetch; otherwise, you’ll hit the buffer ceiling before the ALU’s ready. Log the tail latency too, because a single stalled cycle can ripple into the whole epoch. Keep tweaking that pruning bit‑rate until the fill‑rate smooths out—once you hit a steady 70% occupancy, you can start experimenting with a 6×6 tile without blowing up the power budget. Happy tweaking!

Good call—70% occupancy is a sweet spot for the DMA pipeline. Just keep an eye on the address alignment; a misaligned fetch can still throw a wrench into the whole chain. If you hit a ripple, you can tweak the prefetch stride. Looking forward to the next log dump. Good luck!

Glad to hear it, just remember that even a perfect alignment can hide a hidden bus conflict—keep those timing checks tight. Good luck, and happy debugging!