15.5 Event-Based Spatially Zooming Neural Interface IC with 10nW/Input Reconfigurable-Inverter Fabric and Input-Adaptive Quantization
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Bibliographic record
Abstract
Large-scale neural interface ICs have tens of thousands of electrodes [1]–[3], enabling a wide range of applications including neural prostheses and therapeutic neuromodulation. However, the human brain contains 86 billion neurons and new frontiers in brain interfacing, such as understanding memory and cognition, will benefit from concurrent access to a million or more of implanted electrodes [4]. Modern microfabrication technologies, including silicon wafer thinning [1], allow for dense co-integration of electrodes and transistors on the same flexible substrate [1,4-6] and overcome the issue of the mega-scale electrode interconnect bottleneck [7]. The key remaining challenges are the low energy efficiency of neural ADCs and the high output data rate [4]. For example, one million inputs require 10nW/input ADC power - for a tissue-safe 10mW ADC total power budget [8], and a 200Gb/s wireless link - for 8b conversion at 25kHz [4]. However, the power dissipation of neural ADCs, either dedicated [9]–[12] or time-multiplexed [1-3,13], is over 50× higher, and implantable radios are at least 100× slower [14]–[15]. To address these challenges, neural spiking sparsity has been exploited in both off-line [16]–[17] and on-line [8], [18] methods of optimum electrode selection, but this leads to significant losses in recorded information [17] and requires <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{near}-\cup \mathrm{W}/\text{inp}\lfloor\vert \mathrm{t}$</tex> power due to static circuit biasing [8], [18], respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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