Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures
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Bibliographic record
Abstract
This paper presents signal processing methods to enhance the energy vs. accuracy trade-off of in-memory computing (IMC) architectures. First, an optimal clipping criterion (OCC) for signal quantization is proposed in order to minimize the precision of column analog-to-digital converters (ADCs) at iso-accuracy. For a Gaussian distributed signal, the OCC is shown to reduce the column ADC precision requirements by 3 bits at an signal-to-quantization noise ratio (SQNR) of 22.5 dB over the commonly used full range (FR) quantizer. Next, the input-sliced weight-parallel (ISWP) IMC architecture is presented as a generalization of the popular bit-serial bit-parallel (BSBP) architecture. Quantization noise analysis of the ISWP indicates that its accuracy is comparable to BSBP while providing an order-of-magnitude reduction in energy consumption due to fewer array invocations and smaller ADC precision. Combining OCC and ISWP noise analysis, we map popular DNNs such as VGG-9 (CIFAR-10), ResNet-18 (CIFAR-10), and AlexNet (ImageNet) on a OCC-enabled ISWP architecture and show a reduction in energy consumption by an order-of-magnitude at iso-accuracy over the BSBP architecture that employs FR-based ADCs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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