`timescale 1ns/1ps module tb; parameter DATA_WIDTH = 8; parameter N_INPUTS = 32; parameter N_NEURONS = 8; parameter PARALLEL = 8; parameter ACC_WIDTH = 32; reg clk; reg rst; reg start; 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; wire signed [DATA_WIDTH*N_NEURONS-1:0] y_bus; wire busy; wire done; integer i; integer errors; layer #( .DATA_WIDTH(DATA_WIDTH), .N_INPUTS(N_INPUTS), .N_NEURONS(N_NEURONS), .PARALLEL(PARALLEL), .ACC_WIDTH(ACC_WIDTH) ) dut ( .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) ); initial begin clk = 0; forever #5 clk = ~clk; end task run_layer; begin @(posedge clk); start = 1; @(posedge clk); start = 0; wait(done == 1); @(posedge clk); end endtask initial begin $dumpfile("sim/layer.vcd"); $dumpvars(0, tb); rst = 1; start = 0; x_bus = 0; weights_bus = 0; bias_bus = 0; errors = 0; repeat (2) @(posedge clk); rst = 0; /* * Input vector: * all inputs = 1 */ for (i = 0; i < N_INPUTS; i = i + 1) x_bus[i*DATA_WIDTH +: DATA_WIDTH] = 8'sd1; /* * Neuron 0: * weights = 1 * bias = 0 * result = 32 */ for (i = 0; i < N_INPUTS; i = i + 1) weights_bus[ 0*N_INPUTS*DATA_WIDTH + i*DATA_WIDTH +: DATA_WIDTH ] = 8'sd1; bias_bus[0*DATA_WIDTH +: DATA_WIDTH] = 8'sd0; /* * Neuron 1: * weights = 3 * result = 96 */ for (i = 0; i < N_INPUTS; i = i + 1) weights_bus[ 1*N_INPUTS*DATA_WIDTH + i*DATA_WIDTH +: DATA_WIDTH ] = 8'sd3; /* * Neuron 2: * weights = -1 * result = -32 -> ReLU = 0 */ 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: * weights = 8 * result = 256 -> INT8 saturation = 127 */ for (i = 0; i < N_INPUTS; i = i + 1) weights_bus[ 3*N_INPUTS*DATA_WIDTH + i*DATA_WIDTH +: DATA_WIDTH ] = 8'sd8; /* * Neuron 4: * weights = 0 * bias = +5 * result = 5 */ bias_bus[4*DATA_WIDTH +: DATA_WIDTH] = 8'sd5; /* * Neuron 5: * weights = 0 * bias = -5 * ReLU = 0 */ bias_bus[5*DATA_WIDTH +: DATA_WIDTH] = -8'sd5; /* * Neuron 6: * weights = 1 * bias = -10 * result = 32 - 10 = 22 */ for (i = 0; i < N_INPUTS; i = i + 1) weights_bus[ 6*N_INPUTS*DATA_WIDTH + i*DATA_WIDTH +: DATA_WIDTH ] = 8'sd1; bias_bus[6*DATA_WIDTH +: DATA_WIDTH] = -8'sd10; /* * Neuron 7: * first 16 weights = 1, remaining 16 = 0 * bias = +5 * result = 16 + 5 = 21 * * Exercises a sparse weight pattern across groups * (PARALLEL = 8 -> GROUPS = 4). */ for (i = 0; i < N_INPUTS; i = i + 1) weights_bus[ 7*N_INPUTS*DATA_WIDTH + i*DATA_WIDTH +: DATA_WIDTH ] = (i < 16) ? 8'sd1 : 8'sd0; bias_bus[7*DATA_WIDTH +: DATA_WIDTH] = 8'sd5; $display(""); $display("=============================="); $display("LAYER TEST (INT8)"); $display("=============================="); run_layer; $display("Neuron 0 = %5d expected = 32", $signed(y_bus[0*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 1 = %5d expected = 96", $signed(y_bus[1*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 2 = %5d expected = 0", $signed(y_bus[2*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 3 = %5d expected = 127", $signed(y_bus[3*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 4 = %5d expected = 5", $signed(y_bus[4*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 5 = %5d expected = 0", $signed(y_bus[5*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 6 = %5d expected = 22", $signed(y_bus[6*DATA_WIDTH +: DATA_WIDTH])); $display("Neuron 7 = %5d expected = 21", $signed(y_bus[7*DATA_WIDTH +: DATA_WIDTH])); if ($signed(y_bus[0*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd32) errors = errors + 1; if ($signed(y_bus[1*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd96) errors = errors + 1; if ($signed(y_bus[2*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd0) errors = errors + 1; if ($signed(y_bus[3*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd127) errors = errors + 1; if ($signed(y_bus[4*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd5) errors = errors + 1; if ($signed(y_bus[5*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd0) errors = errors + 1; if ($signed(y_bus[6*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd22) errors = errors + 1; if ($signed(y_bus[7*DATA_WIDTH +: DATA_WIDTH]) !== 8'sd21) errors = errors + 1; $display("busy = %0d", busy); $display("done = %0d", done); $display(""); $display("=============================="); if (errors == 0) $display("PASS - ALL 8 NEURONS"); else $display("FAIL - %0d ERRORS", errors); $display("=============================="); $display("LAYER TEST FINISHED"); $display(""); $finish; end endmodule