chore: plan GPU acceleration for autodiff tensor operations

GPU_ACCELERATION_TODO.md

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+# GPU Acceleration Implementation Status
+
+## Completed
+
+### GPU Backend (IlgpuBackend.cs)
+- [x] Matrix multiplication (naive + tiled)
+- [x] Transpose
+- [x] Element-wise: Add, Subtract, Multiply, Divide
+- [x] Activations: ReLU, LeakyReLU, ELU, GELU, Swish, Sigmoid, Tanh
+- [x] Math ops: Exp, Log, Sqrt, Power, Abs, Maximum, Minimum
+- [x] Reductions: Sum, Mean
+- [ ] Softmax (GPU kernel) - currently CPU fallback
+
+### Layers with GPU Support (6/74)
+- [x] FeedForwardLayer - forward + backward
+- [x] DenseLayer - forward + backward
+- [x] FullyConnectedLayer - forward
+- [x] ActivationLayer - forward
+- [x] AddLayer - forward
+- [x] MultiplyLayer - forward
+- [ ] 68 other layers need GPU support
+
+### Optimizers (15/15 gradient-based complete)
+- [x] AdamOptimizer - GPU parameter updates
+- [x] MomentumOptimizer - GPU parameter updates
+- [x] StochasticGradientDescentOptimizer - GPU parameter updates
+- [x] RootMeanSquarePropagationOptimizer - GPU parameter updates
+- [x] AdagradOptimizer - GPU parameter updates
+- [x] NadamOptimizer - GPU parameter updates
+- [x] AdaDeltaOptimizer - GPU parameter updates
+- [x] AdaMaxOptimizer - GPU parameter updates
+- [x] AMSGradOptimizer - GPU parameter updates
+- [x] LionOptimizer - GPU parameter updates
+- [x] NesterovAcceleratedGradientOptimizer - GPU parameter updates
+- [x] GradientDescentOptimizer - GPU parameter updates
+- [x] MiniBatchGradientDescentOptimizer - GPU parameter updates
+- [x] ProximalGradientDescentOptimizer - GPU gradient step + CPU regularization
+- [x] FTRLOptimizer - CPU-only (complex thresholding)
+- Note: BFGS, L-BFGS, CMAES use different patterns (see detailed section below)
+
+## High Priority - Common Layers
+
+### Dense/Fully Connected
+- [x] FeedForwardLayer
+- [x] DenseLayer
+- [x] FullyConnectedLayer - same as Dense, add GPU
+
+### Convolutional
+- [ ] ConvolutionalLayer - needs im2col or direct convolution kernel
+- [ ] SeparableConvolutionalLayer
+- [ ] DepthwiseSeparableConvolutionalLayer
+- [ ] DilatedConvolutionalLayer
+- [ ] DeconvolutionalLayer
+
+### Recurrent
+- [ ] LSTMLayer - needs 4 gates implementation
+- [ ] GRULayer - needs 3 gates implementation
+- [ ] RecurrentLayer
+- [ ] BidirectionalLayer
+
+### Normalization
+- [ ] BatchNormalizationLayer - needs mean/variance computation
+- [ ] LayerNormalizationLayer
+
+### Pooling
+- [ ] MaxPoolingLayer - needs reduction kernel
+- [ ] PoolingLayer
+- [ ] GlobalPoolingLayer
+
+### Attention
+- [ ] MultiHeadAttentionLayer - critical for transformers
+- [ ] SelfAttentionLayer
+- [ ] AttentionLayer
+
+### Transformer Components
+- [ ] TransformerEncoderLayer
+- [ ] TransformerDecoderLayer
+- [ ] PositionalEncodingLayer
+
+## Medium Priority
+
+### Activation Layers
+- [x] ActivationLayer - route to GPU activations
+
+### Embedding
+- [ ] EmbeddingLayer - lookup table on GPU
+- [ ] PatchEmbeddingLayer
+
+### Dropout/Regularization
+- [ ] DropoutLayer - random mask generation on GPU
+- [ ] GaussianNoiseLayer
+
+### Combination Layers
+- [x] AddLayer - element-wise add
+- [x] MultiplyLayer - element-wise multiply
+- [ ] ConcatenateLayer - tensor concatenation
+
+### Reshaping
+- [ ] FlattenLayer - reshape operation
+- [ ] ReshapeLayer
+
+## Low Priority - Specialized
+
+### Advanced Architectures
+- [ ] ResidualLayer
+- [ ] HighwayLayer
+- [ ] GatedLinearUnitLayer
+- [ ] SqueezeAndExcitationLayer
+
+### Capsule Networks
+- [ ] CapsuleLayer
+- [ ] PrimaryCapsuleLayer
+- [ ] DigitCapsuleLayer
+
+### Graph Neural Networks
+- [ ] GraphConvolutionalLayer
+
+### Memory Networks
+- [ ] MemoryReadLayer
+- [ ] MemoryWriteLayer
+- [ ] TemporalMemoryLayer
+
+### Specialized
+- [ ] MixtureOfExpertsLayer
+- [ ] QuantumLayer
+- [ ] SpikingLayer
+- [ ] ReservoirLayer
+- [ ] RBFLayer
+- [ ] RBMLayer
+- [ ] ConvLSTMLayer
+- [ ] SpatialTransformerLayer
+- [ ] SubpixelConvolutionalLayer
+- [ ] LocallyConnectedLayer
+- [ ] ConditionalRandomFieldLayer
+
+## Gradient-Based Optimizers (15/15 complete)
+
+- [x] AdamOptimizer - GPU parameter updates
+- [x] MomentumOptimizer - GPU parameter updates
+- [x] StochasticGradientDescentOptimizer - GPU parameter updates
+- [x] RootMeanSquarePropagationOptimizer (RMSProp) - GPU parameter updates
+- [x] AdagradOptimizer - GPU parameter updates
+- [x] NadamOptimizer - GPU parameter updates
+- [x] AdaDeltaOptimizer - GPU parameter updates
+- [x] AdaMaxOptimizer - GPU parameter updates
+- [x] AMSGradOptimizer - GPU parameter updates
+- [x] LionOptimizer - GPU parameter updates
+- [x] NesterovAcceleratedGradientOptimizer - GPU parameter updates
+- [x] GradientDescentOptimizer - GPU parameter updates
+- [x] MiniBatchGradientDescentOptimizer - GPU parameter updates
+- [x] ProximalGradientDescentOptimizer - GPU gradient step + CPU regularization
+- [x] FTRLOptimizer - CPU-only (complex thresholding logic)
+
+## Second-Order & Non-Gradient Optimizers (Not Applicable for GPU Parameter Updates)
+
+- BFGSOptimizer - Uses Hessian approximation, line search (different pattern)
+- LBFGSOptimizer - Uses limited-memory Hessian, line search (different pattern)
+- CMAESOptimizer - Evolution strategy, non-gradient-based (different pattern)
+
+Note: The above optimizers don't use the UpdateParameters(params, gradient) pattern
+and would require custom GPU implementations specific to their algorithms.
+
+## Loss Functions
+
+- [ ] MSE - GPU kernel needed
+- [ ] CrossEntropy - GPU kernel needed
+- [ ] BinaryCrossEntropy - GPU kernel needed
+- [ ] All other loss functions
+
+## Missing GPU Operations
+
+- [ ] Convolution kernels (im2col, direct, winograd)
+- [ ] Proper Softmax GPU kernel (with shared memory reduction)
+- [ ] Max reduction for pooling
+- [ ] Dropout mask generation
+- [ ] Batch normalization statistics
+- [ ] Embedding lookup
+
+## Tests Needed
+
+- [ ] GPU activation function tests (LeakyReLU, ELU, GELU, Swish)
+- [ ] GPU math operation tests (Exp, Log, Sqrt, Power, Abs, Max, Min)
+- [ ] DenseLayer GPU forward/backward tests
+- [ ] AdamOptimizer GPU parameter update tests
+- [ ] Additional layer GPU tests as implemented
+- [ ] Performance benchmarks for all GPU ops
+
+## Current Status
+
+**Layers**: 6/74 complete (8.1%)
+**Gradient-Based Optimizers**: 15/15 complete (100%)
+**Operations**: 17+ GPU kernels implemented
+**Backward passes**: FeedForwardLayer, DenseLayer have GPU backward
+
+All common gradient-based optimizers now support GPU acceleration for large parameter sets!