Subversion Repositories keras_to_fpga

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//////////////////////////////////////////////////////////////////////

module

  axis_dense

  #(

    int N,

    int SHAPE[],

    string LAYER,

    string DIR,

    string ACTIVATION

  )

  (

    axis_if axis_in,

    axis_if axis_out,

    wire    error,

    input   aclk,

    input   aresetn

  );

  // --------------------------------------------------------------------

  initial

    $display("### layer: %s SHAPE = (%0.d, %0.d) | (inputs, neurons) | %s", LAYER, SHAPE[0], SHAPE[1], ACTIVATION);

  // --------------------------------------------------------------------

  localparam A = $clog2(SHAPE[0]+1);

  localparam ROM_SIZE = 2**A;

  localparam START = 0;

  localparam STOP = SHAPE[0]+1;

  localparam MA = $clog2(SHAPE[1]); // fixme

  // --------------------------------------------------------------------

  axis_if #(N) axis_neuron[(2**MA)-1:0](.*);

  wire [SHAPE[1]-1:0] neuron_error;

  generate

    for(genvar j = 0; j < 2**MA; j++)

      if(j < SHAPE[1])

      begin: neuron_gen

        localparam FILE_NAME = $sformatf("%s/%s_%0.d.txt", DIR, LAYER, j);

        axis_dense_neuron #(N, A, FILE_NAME, START, STOP, ACTIVATION)

          neuron_i(.axis_out(axis_neuron[j]), .error(neuron_error[j]),  .*);

      end

      else

      begin: dummy_gen

        assign axis_neuron[j].tvalid = 1;

        assign axis_neuron[j].tdata = 0;

        assign axis_neuron[j].tlast = 1;

      end

  endgenerate

  // --------------------------------------------------------------------

  generate

  begin: catenate_gen

    if(SHAPE[1] > 1)

        recursive_axis_catenate #(.N(N), .MA(MA))

          recursive_axis_catenate_i(.axis_in(axis_neuron), .*);

    else

      axis_alias axis_alias_in(axis_neuron[0], axis_out);

  end

  endgenerate

  // --------------------------------------------------------------------

  assign error = |neuron_error;

// --------------------------------------------------------------------

endmodule