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Task-specific AI accelerators

How large must an activation buffer be before the best dataflow for a small keyword-spotting accelerator changes?

research/accel-dataflowbuf

How large a bounded LRU activation buffer must be before output-stationary stops being the best dataflow for the 40 accel-kws models at batch 4: 32, 64 and 128 words at 8, 16 and 32 lanes (for 10 to 30 of 40 models, input-stationary wins), and not at all at batch 1; buffer-read energy is assumed and no area is charged (stylised).

4 tests

What it found

A bounded LRU activation buffer of K 8-bit words added to the four accel-dataflow schedules and swept over the 40 accel-kws models at 8, 16 and 32 lanes. At batch 1 output-stationary wins every model at every K. At batch 4 input-stationary first ties or beats it at K = 32, 64 and 128 words (8, 16 and 32 lanes; 10 of 40 models at 8 and 16 lanes, 30 at 32), and no flip occurs for the 2-bit models. If a buffer read costs half an SRAM read instead of a tenth, the flips mostly disappear. Buffer-read energy is assumed and buffer area is not charged. Stylised.

A figure from its own output

Figure produced by research/accel-dataflowbuf: buffer
accel-dataflowbuf/experiments/buffer.png, written by the project's own experiment code.

Paper, code and results

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.