feat(v2): scaffold hardware/v1 frozen baseline + M1 Neural Processor

Begins the V2 Neural Multiprocessor / Dataflow architecture per
docs/v2-description.md, per explicit user request to freeze V1 and
start V2 development, copying from V1 what's needed.

Scaffold:
- hardware/v1/: byte-exact, read-only copy of the current V1 codebase
  (rtl, testbenches, tools, constraints, a representative subset of
  synthesis results, and reference docs) -- verified identical via
  diff/cmp against the live top-level tree before being made
  filesystem-read-only. The live top-level tree is untouched and
  remains the project's "production" V1 (see hardware/v1/README.md
  and hardware/v2/logs/decisions.log DEC-0001 for why copy-not-move).
- hardware/v2/: mandatory structure (rtl/sim/constraints/synthesis/
  reports/scripts/logs/docs) plus the full logging system required by
  the spec (development/architecture/simulation/synthesis/timing/
  benchmark/decisions/experiments/errors.log).

M1 -- Neural Processor (hardware/v2/rtl/neural_processor.v):
- 8-stage pipelined perceptron unit (P_IN=8): input align, 8
  multipliers, 3-level adder tree, accumulator, bias+activation, INT8
  saturation. Genuine 1-tile/cycle throughput, not just a wider
  combinational datapath.
- 7-state FSM (NP_IDLE..NP_ERROR per docs/v2-description.md §6, with
  4 baseline states merged into NP_WAIT_OPERANDS -- see
  decisions.log DEC-0002); valid/ready/data/last stream interfaces
  per §7.
- Bit-exact vs the frozen hardware/v1/rtl/neuron_parallel.v + mac8.v
  + mac_unit.v: 7/7 tests pass (hardware/v2/sim/tb_neural_processor.v),
  covering regular/mixed-sign/extreme-INT8 vectors, both activations,
  a zero-idle-gap back-to-back-tiles throughput check, and an 8-tile
  job -- verified with Verilator (see below for why).
- Real synthesis + place&route (Yosys + nextpnr-ecp5): 0 CHECK
  problems, Fmax 183.12 MHz at ACC_WIDTH=32 (PASS at 80MHz, ~3x V1's
  isolated PARALLEL=8 Fmax of 61.71 MHz) and 176.21 MHz at ACC_WIDTH=24
  (a user-requested comparison experiment, also bit-exact-verified;
  see experiments.log EXP-0001/EXP-0002 and benchmark.log).

Three real bugs found and resolved during M1 development (full
diagnostic record in errors.log):
- Two independent, reproducible Icarus Verilog v13.0 scheduling
  defects (ERR-0001, ERR-0002) that silently produced wrong simulation
  results for standard sequential Verilog -- confirmed via Verilator
  5.050 giving correct results on the same minimal repros. Verilator
  is now the trusted simulator for hardware/v2/ (decisions.log
  DEC-0004); Icarus's affected protocol-violation check was removed
  from the RTL and deferred architecturally to the Neural Director
  (DEC-0003) rather than chased further.
- One real RTL bug (ERR-0003): last0 wasn't gated like valid0,
  letting a "last tile" tag leak into the pipeline ahead of its
  actual valid tile on back-to-back jobs. Fixed and verified.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013xXuuRUWZScuo1DeYJxs3v

hardware/v1/synthesis/p4/top.v

@@ -0,0 +1,78 @@
+module top (
+    input clk,
+    input rst,
+    input start,
+
+    output signed [31:0] y_bus,
+    output busy,
+    output done
+);
+
+    localparam DATA_WIDTH = 8;
+    localparam N_INPUTS   = 256;
+    localparam N_NEURONS  = 4;
+    localparam PARALLEL   = 4;
+    localparam ACC_WIDTH  = 32;
+
+    reg signed [DATA_WIDTH*N_INPUTS-1:0] x_bus;
+    reg signed [DATA_WIDTH*N_INPUTS*N_NEURONS-1:0] weights_bus;
+    reg signed [DATA_WIDTH*N_NEURONS-1:0] bias_bus;
+
+    integer i;
+
+    initial begin
+        x_bus       = 0;
+        weights_bus = 0;
+        bias_bus    = 0;
+
+        // X = 1
+        for (i = 0; i < N_INPUTS; i = i + 1)
+            x_bus[i*DATA_WIDTH +: DATA_WIDTH] = 8'sd1;
+
+        // Neuron 0: first 32 weights = +1
+        for (i = 0; i < 32; i = i + 1)
+            weights_bus[
+                0*N_INPUTS*DATA_WIDTH +
+                i*DATA_WIDTH +:
+                DATA_WIDTH
+            ] = 8'sd1;
+
+        // Neuron 1: all zero, bias = 10
+        bias_bus[1*DATA_WIDTH +: DATA_WIDTH] = 8'sd10;
+
+        // Neuron 2: all weights = -1
+        for (i = 0; i < N_INPUTS; i = i + 1)
+            weights_bus[
+                2*N_INPUTS*DATA_WIDTH +
+                i*DATA_WIDTH +:
+                DATA_WIDTH
+            ] = -8'sd1;
+
+        // Neuron 3: first 32 weights = +4
+        for (i = 0; i < 32; i = i + 1)
+            weights_bus[
+                3*N_INPUTS*DATA_WIDTH +
+                i*DATA_WIDTH +:
+                DATA_WIDTH
+            ] = 8'sd4;
+    end
+
+    layer #(
+        .DATA_WIDTH(DATA_WIDTH),
+        .N_INPUTS(N_INPUTS),
+        .N_NEURONS(N_NEURONS),
+        .PARALLEL(PARALLEL),
+        .ACC_WIDTH(ACC_WIDTH)
+    ) u_layer (
+        .clk(clk),
+        .rst(rst),
+        .start(start),
+        .x_bus(x_bus),
+        .weights_bus(weights_bus),
+        .bias_bus(bias_bus),
+        .y_bus(y_bus),
+        .busy(busy),
+        .done(done)
+    );
+
+endmodule