Files
FPGA-Neural/synth/ecp5/p4/top.v
T
michele cfd5e98a0e feat: add FPGA-Neural benchmark tooling + same-tier ECP5 P2/P4/P8 results
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.
2026-09-02 19:48:03 +02:00

79 lines
1.8 KiB
Verilog

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