Adds FPGA-exclusive access to the onboard W25Q128JV SPI NOR flash for
weights/bias/network persistence, layered as spi_flash_master (raw SPI,
USRMCLK-driven) -> flash_copy_engine (flash<->PSRAM streaming, erase-
before-write, Page Program loop) -> flash_slot_manager (16-slot catalog
with CRC32), exposed via 8 new SPI opcodes (0x40-0x47). Fixes two
pre-existing bugs found during bring-up: a psram_controller.v request
lost during power-up, and a one-cycle-pulse race in the PSRAM arbiter
request handshake. Full simulation + real Yosys/nextpnr-ecp5 synthesis
verification (0 errors, Fmax 66.68MHz) in WORKLOG.md and
docs/FPGA-Neural-Flash-Subsystem-Verification.md.
Also updates docs/pinout to reflect the 56-signal real .lpf (3 new
flash pins) and documents the WRITE_RAM/READ_RAM host backpressure
risk found while testing this subsystem.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013xXuuRUWZScuo1DeYJxs3v
PSRAM page-mode read burst support in psram_controller.v: enables the
ISSI IS66WVE4M16EBLL-70BLI's page mode via its configuration-register
software-access sequence at boot (disabled by default on the real
chip), then keeps CE#/OE# asserted after a read so a same-page
continuation only pays tAPA (20ns) instead of a full tAA (70ns)
random access, with automatic tCEM-safe session closing. Only a WRITE
closes the page -- byte-enable changes do not, since
int8_memory_access.v alternates them on nearly every access and an
early implementation attempt that treated them as a close condition
measured a real regression (53.25->61.25 cycles/edge) before being
corrected (53.25->37.53 cycles/edge, +42% gather bandwidth).
sim/psram_model.v gained independent tAPA/tAA and tCEM enforcement
(with a real Verilog same-timestep event-ordering race found and
fixed via a #0 sync) so the regression proves real timing compliance,
not just data correctness. New sim/psram_page_mode_tb.v; full 26-file
regression suite re-run clean. Real nextpnr-ecp5 Fmax re-measured on
the full spi_neuron_top system: 75.73MHz (P2, up from 55.59MHz) and
65.13MHz (P8) -- still under the 80MHz target but not regressed, with
the critical path confirmed (not assumed) to remain entirely inside
neuron_parallel's accumulate chain, never psram_controller.
Also includes this session's other already-validated work: the graph
engine (Type #2 sparse-graph network: act_buffer, graph_engine,
netasm host assembler), real CABGA381 pinout (.lpf, place&route
verified) and physical IRQ_N/DATA_READY_N pins, and Phase 7 timing
closure logs -- all previously uncommitted, documented in WORKLOG.md.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LH3jPeJ3eFMfF2v8SQhpkk
Yosys + nextpnr-ecp5 synthesis of the extended spi_neuron_top.v
(layer_sequencer + mux + arbiter Port C wired in), speed grade -8,
N_INPUTS=32/N_NEURONS=1: PARALLEL=8 -> 40.57 MHz FAIL, PARALLEL=2 ->
42.54 MHz FAIL. Both worse than Phase 4 alone (~52.58/~55.85 MHz for
the identical configs) -- the Phase 5 wiring cost real timing
headroom. Critical path also shifted from Phase 4's saturation-
comparator finding to neuron_memory's x_mem/w_mem LUT-RAM read-mux
tree feeding the accumulator. Full analysis and candidate next steps
(block RAM for x_mem/w_mem, pipelining, a seed sweep to separate
placement noise from a structural bottleneck) recorded under Phase 7
in docs/FPGA-NeuralNetwork-Engine.md.
tools/fpga_benchmark.py: parametric Yosys + nextpnr-ecp5 benchmark
harness for the LFE5U-45F-8BG381 (speed grade -8, 80 MHz target),
sweeping PARALLEL over the neuron layer and parsing Fmax/LUT4/DFF/DSP
utilization out of the nextpnr report into JSON/CSV.
synth/ecp5/p2, p4, p8: real synthesis+PnR results backing the
same-price-tier FPGA comparison (P2: 87.88 MHz PASS, P4: 75.01 MHz
FAIL, P8: 147.62 MHz PASS -- non-monotonic, dominated by placement
noise since the whole design uses <2% of the device's LUT4 fabric at
every setting, and P8 notably maps to 0 DSP blocks vs 8/16 for P2/P4).
synth/ecp5/top.v: benchmark harness top-level, reworked to generate
deterministic non-constant X/weights/bias via `keep`-attributed
generate blocks so Yosys can't constant-fold the datapath away.
Also adds .gitignore for Python's __pycache__/*.pyc.
micheleandClaude Sonnet 5