Bumps ADDR_WIDTH's default from 22 to 23 bits across every RTL
module (neuron_memory, layer_sequencer, spi_engine, spi_neuron_top,
mem_arbiter, int8_memory_access, memory_interface, psram_controller,
memory_model) and every testbench that mirrors it, so the system's
byte-address space reaches the full 8 MiB the recommended PSRAM part
(ISSI IS66WVE4M16EBLL-70BLI, docs/FPGA-Neural-Hardware-Design.md §3)
actually provides -- previously only 4 MiB (half the chip) was
reachable, since int8_memory_access.v's byte->word address shift
(addr >> 1) turned the old 22-bit byte address into only 21 real word
bits, one short of the chip's real 22-bit word address (A0-A21). At
23 bits, that same shift lands exactly on all 22 chip address lines,
so the whole part is usable now instead of deferred to a future
widening.
Also fixes a stray 22'd11-sized literal in layer_sequencer.v's
descriptor-table address increment (numerically already safe via
Verilog's zero-extension, but now correctly unsized so it always
matches ADDR_WIDTH instead of silently assuming 22).
Updated docs/FPGA-NeuralNetwork-Engine.md's SPI protocol address-field
note (23 bits, top 1 reserved bit instead of 2) and
docs/FPGA-Neural-Hardware-Design.md's PSRAM section (the "chip has
one spare address line" framing is gone now that all 22 are wired
and used).
Full regression (all 11 ADDR_WIDTH-touching testbenches, plus a
Yosys elaboration check of spi_neuron_top with the new default and
no override) passes clean.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WQV3vS9TXaGDJ5cRfnfidt
Two related Phase 5 additions, both threaded the same way (a new
runtime field defaulting to the pre-existing behavior, settable
per-layer via the descriptor table or per-run via SET_BASE):
Configurable activation functions:
- neuron_parallel.v gains a 2-bit `activation` port (ACT_NONE =
linear + two-sided INT8 saturate, ACT_RELU = the original
hardwired behavior, kept as the default so every pre-existing
caller/testbench is unaffected), threaded through neuron_memory.v.
- spi_engine.v: SET_BASE sel=6 (single-layer path); the descriptor
table gains a 7th byte (multi-layer path).
- Verified in neuron_parallel_tb.v (negative pass-through + negative
saturation to -128) and end-to-end in
spi_neuron_top_runnetwork_tb.v (a real negative accumulator that
ACT_RELU would clamp to 0 comes through unclamped under ACT_NONE,
over real SPI/RAM).
Runtime network width (one bitstream, any topology up to its
build-time max, entirely host-configured over SPI):
- neuron_parallel.v gains n_inputs_real, bounding its MAC group loop
(n_inputs_real/PARALLEL groups instead of the fixed build-time
count). neuron_memory.v gains n_inputs_real/n_neurons_real,
bounding its X/W RAM-read loop and its neuron loop. All default to
the build-time max, so unconnected callers are unaffected.
n_inputs_real must stay a multiple of PARALLEL (same constraint
N_INPUTS itself is held to at elaboration time, now the caller's
runtime responsibility).
- spi_engine.v: SET_BASE sel=7/8 (single-layer path); the descriptor
table grows to 11 bytes/layer (+n_inputs_real +n_neurons_real,
multi-layer path) -- layer_sequencer.v also now copies only
n_neurons_real bytes into the ping-pong buffer, not the full
build width.
- This is real early termination, not bookkeeping: no RAM
zero-padding needed for the unused tail, and it measurably
completes faster. neuron_parallel_tb.v TEST 7: 3 cycles vs 6 for a
reduced-vs-full run, with garbage loaded into the skipped lanes to
prove they're never read. neuron_memory_tb.v TEST 5: through the
real PSRAM stack, 209 cycles vs 788. layer_sequencer_tb.v proves a
reduced n_neurons_real shortens the ping-pong copy-out itself
(bytes beyond the real count stay untouched, not just differing).
docs/FPGA-NeuralNetwork-Engine.md: §8.1 opcode/SET_BASE table, new
"Runtime network width" subsection, Phase 5 checklist, Current
Status table, and the "Core architectural principle" statement
updated to reflect that topology (not just trained parameters) is
now host-configured at runtime up to a build-time ceiling.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WQV3vS9TXaGDJ5cRfnfidt
Wires the already-present layer_sequencer.v into the SPI stack:
- spi_engine.v: RUN_NETWORK opcode (0x23) + SET_BASE selectors for
table_base/buf_a_base/buf_b_base; STATUS.busy/done extended to
track the sequencer (seq_busy/seq_done) alongside neuron_memory
directly, so done latches on the last layer only.
- spi_neuron_top.v: instantiates layer_sequencer, muxes
neuron_memory's control inputs between it (while seq_busy) and
spi_engine's direct-drive path (legacy single-layer mode), wires
the sequencer's own RAM master to mem_arbiter's Port C.
Found and fixed a real race while writing the end-to-end test: STATUS's
sticky/clear-on-read done bit read its value live/combinationally
during transmission and cleared unconditionally on any STATUS read.
A done_event landing mid-transmission of a STATUS response byte could
be silently dropped -- the host would receive a stale byte while the
sticky bit was cleared regardless, hanging any host polling STATUS in
a loop. Present since Phase 4, not RUN_NETWORK-specific; only
surfaced under this test's continuous polling. Fixed by latching a
status_snapshot at opcode-accept time and gating the clear on what
was actually transmitted.
Tests: spi_engine_tb.v gains RUN_NETWORK/SET_BASE opcode tests (K/L);
new layer_sequencer_tb.v unit-tests the sequencer FSM directly
(descriptor table, ping-pong buffer addressing, byte-exact copy-out);
new spi_neuron_top_runnetwork_tb.v drives a real 2-layer network over
simulated SPI end to end (real neuron_memory + PSRAM, hand-computed
expected output) and confirms the legacy single-layer path still
works afterward. All existing testbenches still pass.
micheleandClaude Sonnet 5