Is the OpenRAM access energy in SKY130 liberty files an energy at all, and does accel-pdkprice's weight-stationary verdict survive?

What it found

accel-pdkprice read the clk0 internal_power of four OpenRAM SRAM macros as pJ per access. Reading the generator source at a pinned commit (five tested facts) shows the value is an average power in mW over exactly one clock cycle, so the access energy is the value times the characterisation period T, which the shipped files do not give (their minimum_period reads 0.09 to 0.1 ns, not usable). Sweeping T = 0.1 to 20 ns over the 64 cells of accel-pdkprice (it reproduces all 64 verdicts at 1 ns): with one weight per access, register-file weight-stationary beats output-stationary in all 32 cells from 3 ns up; with weights packed it wins 0 of 32 at 0.5 ns, 6 at 2, 17 at 5 and 29 at 20 ns (flip periods 0.8 to 31 ns). Partial sums in SRAM lose at every T because the scale cancels. Library values read from source, not a measurement; T is swept, not sourced, because no file gives it; the shipped libraries may predate the pinned source.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
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More in Task-specific AI accelerators

• Once the MFCC front end is counted, does the energy-optimal keyword model change?Joining accel-cascade's committed results with accel-frontend's 46.8 nJ per frame: cascades' classifier-only saving of up to 28% becomes at most 0.42% of whole-system energy, while the most accurate model (about 0.75 test accuracy) costs 23 to 26 times the cheapest classifier but only 5% more system energy (modelled, not measured)
• How big a keyword-spotting model fits a real Tiny Tapeout tile once the hidden layer goes past 64 units?Synthesising the streaming keyword-spotting engine at 96, 128, 192 and 256 hidden units (SKY130 HD, Yosys): area grows about 250 µm² per unit, so H = 128 (0.752 accuracy, 4-bit) needs a 2x2 tile (75,603 µm²) against a 1x2 (36,347 µm²) for H = 64 (0.737): +0.015 accuracy for twice the tile; H = 256 needs 4x2 at 50% utilisation, and its 8-bit layer-2 accumulator overflows 20 bits on a random model.
• Do trained keyword-spotting models fit a Tiny Tapeout tile's 16-bit bias and 20-bit accumulator?All 96 retrained keyword-spotting models fit the Tiny Tapeout engine's 16-bit bias and 20-bit accumulator limits (worst use 43% and 30%), accuracy through the tile contract equals the reference, and 32 trained models run bit-exact in RTL.
• Does Tiny Tapeout's real tile-size table change which keyword-spotting model fits a tile?Tiny Tapeout's real SKY130 tile sizes (1x1 is 161 × 111.52 µm = 17,955 µm², 0.43% below the value assumed earlier) applied to the synthesised keyword-spotting engine and the retrained models: the best 1x1 model is unchanged at 100% and 50% utilisation and flips at 70% (H = 16, 4-bit, 0.596 instead of 8-bit, 0.616, a 29 µm² miss); H = 64 with 8-bit weights (0.737) fits a 1x2 tile.
• How small can a keyword-spotting accelerator get, and what does each bit of precision cost?Keyword-spotting accelerator: quantised MLP on Speech Commands, serial-MAC RTL bit-exact against an integer reference, accuracy vs cycles, cells and modelled energy over width and weight bits (stylised).
• Does a keyword-spotting accelerator fit in a Tiny Tapeout tile, and how accurate can it be?A streaming keyword-spotting engine with no weight memory, exact against an integer reference and synthesised to SKY130 on 32 geometries (6,300 to 24,500 um2); 16 hidden units at 8-bit weights is the most accurate (0.616) that fits one assumed 1x1 Tiny Tapeout tile at 70% utilisation, 64 units at 4 bits (0.738) needs two. Stylised: synthesis only.

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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.