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ANE
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Author SHA1 Message Date
Andy Huang aedb036f08 Optimize ANE training with weights-as-tensors, add inference and benchmarking tools 2026-03-03 14:10:44 +11:00
m0at 40d3f45631 Add ANE probe tests and training telemetry for M5 optimization 
Four standalone probe tests to characterize the M5 ANE:
- test_weight_reload: Can weights be hot-swapped via unload+load without recompilation?
- test_perf_stats: Enumerate _ANEPerformanceStats methods/properties and hardware counters
- test_qos_sweep: Measure compile/load/eval latency across QoS 0-63
- test_ane_advanced: Probe SharedEvents, weightsBuffer IOSurface, procedureIndex, VirtualClient

Training telemetry (train_large.m):
- JSON lines to stderr with per-step timing breakdown and per-batch TFLOPS metrics
- Enables external monitoring tools to visualize ANE utilization in real-time

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-01 22:54:58 -08:00
maderix 4d67db1bdb stories110M: 12-layer ANE training with dashboard, 107ms/step 
- Scale to full stories110M (109M params, 12 layers) with real TinyStories data
- vDSP-vectorized cross-entropy (110ms→14ms), NEON fp16 IO, async dW
- TUI dashboard: loss curve, ANE/CPU power, CPU/memory graphs, text generation
- Split into modular headers: config, io, mil, cpu_ops
 2026-03-01 03:14:39 -08:00
maderix f213c8db68 Initial release 2026-02-28 00:22:06 -08:00