micheleandClaude Sonnet 5
4b5cd4e558
Both testbenches instantiated their DUTs with a FRAC_BITS parameter and
Q8.8 fixed-point 16-bit values, which no longer exist in rtl/neuron_parallel.v
(now plain INT8, DATA_WIDTH=8, hardcoded +127 saturation, ReLU-only clamp).
This made both tests fail elaboration ("parameter FRAC_BITS not found").
Rewrote both benches with integer INT8 stimuli and expectations matching
the current core (no RTL changes): neuron_parallel_tb covers a mixed
vector, ReLU, positive saturation, and mixed values with a boundary
negative bias; layer_tb covers 8 neurons exercising scale, ReLU,
saturation, bias-only, and a sparse weight pattern across groups.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WQV3vS9TXaGDJ5cRfnfidt
236 lines
6.1 KiB
Verilog
236 lines
6.1 KiB
Verilog
`timescale 1ns/1ps
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module tb;
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parameter DATA_WIDTH = 8;
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parameter N_INPUTS = 32;
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parameter N_NEURONS = 8;
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parameter PARALLEL = 8;
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parameter ACC_WIDTH = 32;
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reg clk;
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reg rst;
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reg start;
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reg signed [DATA_WIDTH*N_INPUTS-1:0] x_bus;
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reg signed [DATA_WIDTH*N_INPUTS*N_NEURONS-1:0]
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weights_bus;
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reg signed [DATA_WIDTH*N_NEURONS-1:0]
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bias_bus;
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wire signed [DATA_WIDTH*N_NEURONS-1:0]
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y_bus;
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wire busy;
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wire done;
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integer i;
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integer errors;
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layer #(
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.DATA_WIDTH(DATA_WIDTH),
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.N_INPUTS(N_INPUTS),
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.N_NEURONS(N_NEURONS),
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.PARALLEL(PARALLEL),
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.ACC_WIDTH(ACC_WIDTH)
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) dut (
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.clk(clk),
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.rst(rst),
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.start(start),
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.x_bus(x_bus),
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.weights_bus(weights_bus),
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.bias_bus(bias_bus),
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.y_bus(y_bus),
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.busy(busy),
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.done(done)
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);
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initial begin
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clk = 0;
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forever #5 clk = ~clk;
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end
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task run_layer;
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begin
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@(posedge clk);
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start = 1;
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@(posedge clk);
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start = 0;
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wait(done == 1);
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@(posedge clk);
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end
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endtask
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initial begin
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$dumpfile("sim/layer.vcd");
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$dumpvars(0, tb);
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rst = 1;
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start = 0;
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x_bus = 0;
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weights_bus = 0;
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bias_bus = 0;
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errors = 0;
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repeat (2) @(posedge clk);
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rst = 0;
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/*
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* Input vector:
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* all inputs = 1
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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x_bus[i*DATA_WIDTH +: DATA_WIDTH] = 8'sd1;
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/*
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* Neuron 0:
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* weights = 1
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* bias = 0
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* result = 32
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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weights_bus[
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0*N_INPUTS*DATA_WIDTH +
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i*DATA_WIDTH +:
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DATA_WIDTH
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] = 8'sd1;
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bias_bus[0*DATA_WIDTH +: DATA_WIDTH] = 8'sd0;
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/*
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* Neuron 1:
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* weights = 3
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* result = 96
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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weights_bus[
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1*N_INPUTS*DATA_WIDTH +
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i*DATA_WIDTH +:
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DATA_WIDTH
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] = 8'sd3;
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/*
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* Neuron 2:
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* weights = -1
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* result = -32 -> ReLU = 0
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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weights_bus[
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2*N_INPUTS*DATA_WIDTH +
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i*DATA_WIDTH +:
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DATA_WIDTH
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] = -8'sd1;
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/*
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* Neuron 3:
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* weights = 8
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* result = 256 -> INT8 saturation = 127
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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weights_bus[
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3*N_INPUTS*DATA_WIDTH +
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i*DATA_WIDTH +:
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DATA_WIDTH
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] = 8'sd8;
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/*
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* Neuron 4:
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* weights = 0
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* bias = +5
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* result = 5
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*/
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bias_bus[4*DATA_WIDTH +: DATA_WIDTH] = 8'sd5;
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/*
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* Neuron 5:
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* weights = 0
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* bias = -5
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* ReLU = 0
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*/
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bias_bus[5*DATA_WIDTH +: DATA_WIDTH] = -8'sd5;
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/*
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* Neuron 6:
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* weights = 1
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* bias = -10
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* result = 32 - 10 = 22
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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weights_bus[
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6*N_INPUTS*DATA_WIDTH +
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i*DATA_WIDTH +:
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DATA_WIDTH
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] = 8'sd1;
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bias_bus[6*DATA_WIDTH +: DATA_WIDTH] = -8'sd10;
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/*
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* Neuron 7:
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* first 16 weights = 1, remaining 16 = 0
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* bias = +5
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* result = 16 + 5 = 21
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*
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* Exercises a sparse weight pattern across groups
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* (PARALLEL = 8 -> GROUPS = 4).
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*/
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for (i = 0; i < N_INPUTS; i = i + 1)
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weights_bus[
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7*N_INPUTS*DATA_WIDTH +
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i*DATA_WIDTH +:
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DATA_WIDTH
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] = (i < 16) ? 8'sd1 : 8'sd0;
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bias_bus[7*DATA_WIDTH +: DATA_WIDTH] = 8'sd5;
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$display("");
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$display("==============================");
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$display("LAYER TEST (INT8)");
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$display("==============================");
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run_layer;
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$display("Neuron 0 = %5d expected = 32", $signed(y_bus[0*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 1 = %5d expected = 96", $signed(y_bus[1*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 2 = %5d expected = 0", $signed(y_bus[2*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 3 = %5d expected = 127", $signed(y_bus[3*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 4 = %5d expected = 5", $signed(y_bus[4*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 5 = %5d expected = 0", $signed(y_bus[5*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 6 = %5d expected = 22", $signed(y_bus[6*DATA_WIDTH +: DATA_WIDTH]));
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$display("Neuron 7 = %5d expected = 21", $signed(y_bus[7*DATA_WIDTH +: DATA_WIDTH]));
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if ($signed(y_bus[0*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd32) errors = errors + 1;
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if ($signed(y_bus[1*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd96) errors = errors + 1;
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if ($signed(y_bus[2*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd0) errors = errors + 1;
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if ($signed(y_bus[3*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd127) errors = errors + 1;
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if ($signed(y_bus[4*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd5) errors = errors + 1;
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if ($signed(y_bus[5*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd0) errors = errors + 1;
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if ($signed(y_bus[6*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd22) errors = errors + 1;
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if ($signed(y_bus[7*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd21) errors = errors + 1;
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$display("busy = %0d", busy);
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$display("done = %0d", done);
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$display("");
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$display("==============================");
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if (errors == 0)
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$display("PASS - ALL 8 NEURONS");
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else
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$display("FAIL - %0d ERRORS", errors);
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$display("==============================");
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$display("LAYER TEST FINISHED");
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$display("");
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$finish;
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end
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endmodule
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