Network Architectures
TinyMind provides a range of neural network architectures, all as header-only C++ templates with both fixed-point and floating-point support.
| Architecture | Memory (Q8.8, trainable) | Key Advantage |
|---|---|---|
| LSTM, GRU & Liquid (LTC/CfC) | 952 / 808 bytes | Sequential data, temporal patterns; continuous-time cells for irregular sampling |
| Kolmogorov-Arnold Networks | 1,192 bytes | Learnable activation functions |
| Conv & Pooling Layers | 1,825 bytes (1D pipeline) | 1D time-series + 2D spectrogram/image feature extraction, MobileNet-style separable blocks |
| Linear Self-Attention | ~6 KB (mid-range) | Sequence dependency modeling without O(N^2) |
| FFT Layer | 768 bytes (64-pt Q8.8) | Frequency-domain feature extraction |
| Quantized Networks | 128 bytes (packed binary) | 32-64x weight compression |
| Int8 Affine Quantization | int8 weights + int32 accum | TFLite/CMSIS-NN style post-training int8 across dense/conv/pool/BN/LN/softmax/RNN/attention/FFT |
| Mixed Precision | int8 + fp16 + bf16 bridges | qbridge converters between int8 affine / QValue Q-format / float / fp16 / bf16 |
| SIMD Backends | n/a (perf, not capacity) | ISA-capability gates: NEON / SVE / Helium / AVX2 / AVX-512, byte-identical to scalar |
| Mixture of Experts | router + N resident experts | Decouples compute from capacity: 1 of N experts runs per inference (top-1), or k-of-N softmax-blended |