FPGA-Neural/tools/netasm

netasm

Host-side assembler for the FPGA-Neural network engine. Compiles a small pseudo-assembly description of a network (dense Type #1 or sparse-graph Type #2, see the project spec §9) into:

• the exact on-disk byte layout (descriptor table + edge blocks, spec §4), and
• the SPI command sequence (SET_NET_TYPE / SET_BASE / WRITE_RAM / RUN_NETWORK) needed to load and start it.

This is host tooling only. Nothing here runs on the FPGA — see rtl/graph_engine.v and rtl/spi_engine.v for the hardware side of this protocol.

Grammar

; Tipo #1 (dense)
NET dense
INPUTS 256
LAYER 64 relu
LAYER 16 relu
LAYER 4 none
END

; Tipo #2 (graph)
NET graph
INPUTS 4                 ; id 0..3
NEURON n4 relu bias=2
  CONN 0 w=5
  CONN 1 w=-3
NEURON n5 none bias=0
  CONN n4 w=2            ; symbolic reference to n4's output
  CONN 2 w=7
OUTPUT n5
END

; starts a comment that runs to end of line. A CONN <src> w=<int> source is either a bare decimal id (typically one of the network's inputs) or the name of a previously declared NEURON.

For NET dense, only layer sizes and activations are declared — weight/bias values come from a trained model and are loaded by the host separately (unchanged WRITE_RAM flow); netasm's job there is layout (address allocation, PARALLEL-alignment validation, descriptor table, load/run commands).

For NET graph, netasm assigns every neuron's signal id (inputs get 0..N_in-1; every OUTPUT neuron is guaranteed the highest ids, as required by spec §4.4 — reordering non-output neurons is never needed for correctness since the grammar already forces before-you-use declaration order), resolves symbolic CONN references, pads each neuron's edge list to a PARALLEL multiple with zero-weight edges (spec §2.6 — this is a full, physical edge block, not a hint: hardware just streams n_conn_padded real bytes from PSRAM), and emits the descriptor table + edge blocks + load/run command sequence.

Compile-time validation

Catches these before the runtime load-time guard in rtl/graph_engine.v ever would (spec §9's whole point):

• src_id < out_id (no cycles / forward references)
• src_id, out_id < N_TOTAL
• an OUTPUT neuron is never used as another neuron's source
• every CONN reference (symbolic or literal) resolves to a real id
• a neuron's padded connection count fits the hardware's build-time MAX_CONN
• (dense) every layer's real input count is a PARALLEL multiple

Usage

python3 tools/netasm/cli.py <input.netasm> -o <out_prefix> \
    [--parallel 8] [--max-conn 32] [--n-total 4096] \
    [--table-base 0x...] [--edges-base 0x...] [--x-base 0x...] \
    [--out-base 0x...] [--buf-b-base 0x...] [--weights-base 0x...]

Produces:

• <out_prefix>.frames.bin — length-prefixed SPI transaction bytes (2-byte big-endian length + that many payload bytes, repeated); a host driver replays each record by asserting CS, shifting the bytes out, then deasserting CS.
• <out_prefix>.debug.txt — human-readable id/address/byte dump for review before flashing real hardware.

See examples/graph_example.netasm and examples/dense_example.netasm.

Tests

python3 tools/netasm/tests/test_netasm.py -v

Includes a byte-exact test against the same worked graph example used throughout the RTL testbenches (sim/graph_format_tb.v, sim/graph_engine_tb.v, sim/spi_neuron_top_graph_tb.v), plus one test per compile-time guard above.