An Always-On 3.8 <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS
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Bibliographic record
Abstract
The trend of pushing inference from cloud to edge due to concerns of latency, bandwidth, and privacy has created demand for energy-efficient neural network hardware. This paper presents a mixed-signal binary convolutional neural network (CNN) processor for always-on inference applications that achieves 3.8 μJ/classification at 86% accuracy on the CIFAR-10 image classification data set. The goal of this paper is to establish the minimum-energy point for the representative CIFAR-10 inference task, using the available design tradeoffs. The BinaryNet algorithm for training neural networks with weights and activations constrained to +1 and -1 drastically simplifies multiplications to XNOR and allows integrating all memory on-chip. A weight-stationary, data-parallel architecture with input reuse amortizes memory access across many computations, leaving wide vector summation as the remaining energy bottleneck. This design features an energy-efficient switched-capacitor (SC) neuron that addresses this challenge, employing a 1024-bit thermometer-coded capacitive digital-to-analog converter (CDAC) section for summing pointwise products of CNN filter weights and activations and a 9-bit binary-weighted section for adding the filter bias. The design occupies 6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 28-nm CMOS, contains 328 kB of on-chip SRAM, operates at 237 frames/s (FPS), and consumes 0.9 mW from 0.6 V/0.8 V supplies. The corresponding energy per classification (3.8 μJ) amounts to a 40× improvement over the previous low-energy benchmark on CIFAR-10, achieved in part by sacrificing some programmability. The SC neuron array is 12.9× more energy efficient than a synthesized digital implementation, which amounts to a 4× advantage in system-level energy per classification.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it