Bit-Identical Medical Deep Learning via Structured Orthogonal Initialization

Deep learning training is non-deterministic: identical code with different random seeds produces models that agree on aggregate metrics but disagree on individual predictions, with per-class AUC swings exceeding 20 percentage points on rare clinical classes. We present a framework for verified bit-identical training that eliminates three sources of randomness: weight initialization (via structured orthogonal basis functions), batch ordering (via golden ratio scheduling), and non-deterministic GPU operations (via architecture selection and custom autograd). The pipeline produces MD5-verified identical trained weights across independent runs. On PTB-XL ECG rhythm classification, structured initialization significantly exceeds Kaiming across two architectures (n=20; Conformer p = 0.016, Baseline p < 0.001), reducing aggregate variance by 2-3x and reducing per-class variability on rare rhythms by up to 7.5x (TRIGU range: 4.1pp vs 30.9pp under Kaiming, independently confirmed by 3-fold CV). A four-basis comparison at n=20 shows all structured orthogonal bases produce equivalent performance (Friedman p=0.48), establishing that the contribution is deterministic structured initialization itself, not any particular basis function. Cross-domain validation on seven MedMNIST benchmarks (n=20, all p > 0.14) confirms no performance penalty on standard tasks; per-class analysis on imbalanced tasks (ChestMNIST, RetinaMNIST) shows the same variance reduction on rare classes observed in ECG. Cross-dataset evaluation on three external ECG databases confirms zero-shot generalization (>0.93 AFIB AUC).

Learnable Pulse Accumulation for On-Device Speech Recognition: How Much Attention Do You Need?

Self-attention scales quadratically with sequence length, limiting transformer-based speech models on edge devices. We introduce the Learnable Pulse Accumulator (LPA), an O(n) replacement that substitutes key-query dot products with learned gating functions: content-dependent rectangular pulses, periodic windows, and position-dependent basis functions. An MSE diagnostic sweep determines per-layer replacement difficulty and ordering. Replacing 8 of 12 wav2vec2-base layers yields 10.61% word error rate (WER) on LibriSpeech test-clean, +7.24 percentage points (pp) over the 3.37% baseline, with 3.27x speedup at 120s audio on Apple M4 Pro via an optimized MLX inference path. Cross-domain validation on SepFormer speech enhancement shows all 16 intra-chunk attention layers can be replaced without collapse, suggesting the depth wall arises from linguistic computation rather than an LPA limitation. LPA's near-binary gates at inference enable dense GPU computation with no CPU-GPU synchronization, and all operations map to mobile neural accelerators.