Search Projects Contact Learning pathfrom first bitstream to proven silicon
One route through the whole field, in eight levels. Each builds on the last, and each names three things: what to learn , what to run , and the checkpoint that tells you it stuck. Times assume comfort with Python and basic linear algebra. Read across, or jump to your level.
The route
0
What is stochastic computing? ~30 min
Understand why a stream of random bits can stand in for a number, and how that makes a multiplier one gate.
Checkpoint: you can explain why P(A and B) = P(A)·P(B) for independent Bernoulli streams.
1
Core primitives ~1 h
Encode values into streams and spikes, build a neuron, connect a synapse, and read a firing rate back out.
Checkpoint: you can encode a value, feed it through a synapse into a neuron, and recover the rate.
2
Networks & layers ~2 h
Compose neurons into dense, convolutional and recurrent layers, and run multi-step simulations.
Checkpoint: you can build a two-layer network, run it for a few hundred steps, and plot the spike raster.
3
Learning & plasticity ~2 h
Train with spike-timing plasticity (STDP), reward-modulated three-factor rules, and surrogate gradients.
Checkpoint: you can train a network to classify a simple pattern with STDP or a surrogate gradient.
4
Advanced architectures ~2 h
Reach for convolutional, recurrent, reservoir, hyperdimensional and attention layers, and cortical microcircuits.
Checkpoint: you can classify MNIST with a spiking network, or run a cortical-column or HDC model end to end.
5
Performance & acceleration ~1 h
Move to the compiled multi-language engine, packed-bitstream vectorisation and the GPU path for throughput.
Checkpoint: you can run the accelerated engine from Python and measure its speed-up over the pure-NumPy path on your own machine.
6
Hardware deployment ~2 h
Lower a neuron design onto an FPGA: fixed-point conversion, generated Verilog, co-simulation, synthesis and formal proof.
Checkpoint: you can synthesise a LIF neuron with Yosys, verify it against the Python model, and read its resource report.
7
Research & extension open-ended
Add your own neuron models, wire them into the compiler, and situate the work against the wider field.
Checkpoint: you can add a custom neuron model, connect it to the compiler, and generate HDL for it.