Notes on silicon, arithmetic, and AI for chip design.
Most read · Data formats · Jan 2025
INT8, BFloat, FP8, MSFP, MX — if one factor drove the biggest AI efficiency gains of the past decade, it is the evolution of data formats.
Datapath · Feb 2025
“A bunch of tensors getting multiplied and added” — this is what 70% of AI workloads boil down to. Adders consume 4 to 30 times less energy than multipliers. A walk through what actually happens under the hood when hardware adds two floating-point numbers.
Memory · Dec 2024
On-chip SRAM excels at bandwidth, but relying on it alone is inefficient for LLMs: LLaMA-3-70B needs 140 GB of weights plus several GB of KV cache. The memory footprint math, unpacked.
the asic brew.