Hyperion Roadmap

Overview

Hyperion is a full educational deep learning stack designed to reach expert-level understanding of modern machine learning systems, from tensor computation to Transformer architectures and GPU kernels.

This roadmap defines the multi-stage plan required to build Hyperion from first principles, reaching a level comparable to the engineering mindset of Karpathy, Tri Dao, Olah or Carmack.

The project is divided into five levels of increasing complexity: Foundations, Mini-Framework, Transformer Engine, Systems/HPC, and Mastery.

Level 0 — Foundations

Mathematical Foundations

• Matrix calculus and differentiation rules.
• Directional derivatives and Jacobians.
• Stability of floating-point operations.
• Optimization basics: gradient descent, convexity, proximal ideas.
• Probability fundamentals: entropy, KL divergence, Gaussian families.

Technical Foundations

• Internal design of tensor libraries.
• Basics of CUDA: SMs, warps, memory hierarchy, occupancy.
• Modern C++ concepts: RAII, templates, move semantics.
• HPC fundamentals: cache, SIMD, alignment, memory locality.

Outputs

• Handwritten notes on all topics.
• Small experiments illustrating gradient instability and optimization behavior.

Level 1 — Mini Tensor Framework

Tensor System

• Tensor class with shape, strides, dtype, device field.
• Views, reshape, broadcast, slicing.
• CPU and minimal CUDA backend.

Autograd Engine

• Dynamic computation graph.
• Topological sort for backward.
• Backprop engine supporting basic operations.
• Gradient checkpointing (optional).

Operators

• Element-wise ops: add, mul, exp, log.
• Matrix multiplication.
• Convolution (naive).
• Activations: ReLU, GELU.
• Norm layers: LayerNorm.
• Softmax with numerical stability.
• Dropout.

Optimizers

• SGD and momentum.
• RMSprop.
• Adam and AdamW.
• Learning-rate schedulers (cosine decay).

Outputs

• A working MLP and CNN capable of training on MNIST using Hyperion.

Level 2 — Transformer Engine

Attention Mechanism

• QKV projections.
• Multi-Head Attention.
• Causal and padding masks.
• Stable softmax.
• Rotary positional embeddings (RoPE).
• Pre-LayerNorm architecture.
• Residual path implementation.

GPT Architecture

• GPT block: MHA + feedforward network.
• Token embedding and positional embedding.
• Weight tying.
• Causal masking logic.
• Text generation: greedy, top-k, nucleus sampling.

FlashAttention

• Naive attention baseline.
• Memory-optimized attention variant.
• CPU and vectorized prototype.
• CUDA implementation based on block partitioning.

Outputs

• A functional GPT model trained on a custom dataset.
• A FlashAttention implementation faster than naive MHA.

Level 3 — Systems and High-Performance Computing

CUDA Kernels

• Custom matmul kernels with tiling.
• Softmax kernel.
• LayerNorm kernel.
• Attention QKᵀ kernel.
• Kernel fusion strategies.

Profiling and Optimization

• Warp divergence analysis.
• Memory coalescing and shared memory conflicts.
• Benchmarks using Nsight Systems.
• Performance tuning by adjusting block size and tiling.

Runtime and Architecture

• Custom CPU/GPU memory allocator.
• Operation scheduling.
• Profiling hooks for every operator.
• Minimal runtime similar to tinygrad or low-level PyTorch concepts.

Outputs

• A documented set of optimized kernels.
• A performance report comparing Hyperion’s kernels to naive baselines.

Level 4 — Mastery

Paper Reproductions

• Full reproduction of at least one major ML paper (e.g., FlashAttention, FNO, Neural ODE, Stable Diffusion U-Net, Chinchilla scaling laws).
• Independent experiments validating results.

Simulation and PDE Integration

• FDTD 2D or 3D implementation.
• Perfectly matched layers (PML).
• GPU-accelerated numerical solver.
• Differentiable physics using Hyperion’s autograd.

Scientific Communication

• Long-form technical articles explaining core concepts.
• Visual explanatory pieces inspired by Olah-style notebooks.
• Reproducible code and figures.

Outputs

• Public technical articles.
• A differentiable PDE solver running on Hyperion.
• A complete deep learning educational stack.

Level 5 — Public Release and Recognition

Open-Source Release

• Complete repository with documentation.
• Clear examples and tutorials.
• A design and philosophy page explaining Hyperion’s goals.

Educational Content

• Blog posts and articles explaining the internals of Hyperion.
• Videos or lectures demonstrating the architecture and training process.

Impact Metrics

• Community adoption.
• Repository stars.
• Reposts on technical communities.

Completion Expectations

Completing Levels 1 and 2 places the developer at the top one percent of deep learning practitioners. Completing Levels 3 and 4 places the developer within the rare group capable of building deep learning systems from scratch at a research-level depth. Level 5 establishes public visibility and recognition.