8ビット固定小数点型を使うには、次のように新しいtypedefを定義すればよい：
```
typedef ap_fixed<8,3> fixed_type；
```

これは小数点以下3ビットの符号付き8ビット固定小数点型を定義する。tanh関数の呼び出しを単純な条件文に置き換えることで、線形層の後にRELU非線形性を追加することもできます：

```
fixed_type output_value = weighted_sum + bias[i]；
if (output_value < 0) { 出力値 = 0; 出力値 = 0; 出力値 = 0
    output_value = 0；
}
output_stream.write(output_value)；
```

これらの変更を加えたコード全体を以下に示す：

c
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```
#include "hls_math.h"
#include "hls_stream.h"
#include "ap_fixed.h"

// Define the data types
typedef ap_fixed<8,3> fixed_type;
typedef hls::stream<fixed_type> stream_type;

// Define the linear layer function with RELU nonlinearity
void linear_layer(stream_type& input_stream, fixed_type weight[], fixed_type bias[], stream_type& output_stream) {
    // Set the number of input and output values
    const int N = 1024;

    // Perform the linear layer computation with RELU nonlinearity
    for(int i = 0; i < N; i++) {
        fixed_type input_value = input_stream.read();
        fixed_type weighted_sum = 0;
        for(int j = 0; j < N; j++) {
            weighted_sum += input_value * weight[i * N + j];
        }
        fixed_type output_value = weighted_sum + bias[i];
        if (output_value < 0) {
            output_value = 0;
        }
        output_stream.write(output_value);
    }
}

// Top-level function for the accelerator
void linear_accelerator(stream_type& input_stream, fixed_type weight[], fixed_type bias[], stream_type& output_stream) {
    #pragma HLS INTERFACE axis port=input_stream
    #pragma HLS INTERFACE axis port=output_stream
    #pragma HLS INTERFACE m_axi port=weight offset=slave bundle=gmem
    #pragma HLS INTERFACE m_axi port=bias offset=slave bundle=gmem

    // Call the linear layer function with RELU nonlinearity
    linear_layer(input_stream, weight, bias, output_stream);
}
```

linear_accelerator関数の関数名も、RELU非線形性を用いた新しい実装を反映するように更新したことに注意。
To use an 8-bit fixed-point type, we can define a new typedef as follows:

typedef ap_fixed<8,3> fixed_type;

This defines a signed 8-bit fixed-point type with 3 bits after the decimal point. We can also add a RELU nonlinearity after the linear layer by replacing the tanh function call with a simple conditional statement:

fixed_type output_value = weighted_sum + bias[i];
if (output_value < 0) {
    output_value = 0;
}
output_stream.write(output_value);
The complete code with these changes is shown below:

c
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#include "hls_math.h"
#include "hls_stream.h"
#include "ap_fixed.h"

// Define the data types
typedef ap_fixed<8,3> fixed_type;
typedef hls::stream<fixed_type> stream_type;

// Define the linear layer function with RELU nonlinearity
void linear_layer(stream_type& input_stream, fixed_type weight[], fixed_type bias[], stream_type& output_stream) {
    // Set the number of input and output values
    const int N = 1024;

    // Perform the linear layer computation with RELU nonlinearity
    for(int i = 0; i < N; i++) {
        fixed_type input_value = input_stream.read();
        fixed_type weighted_sum = 0;
        for(int j = 0; j < N; j++) {
            weighted_sum += input_value * weight[i * N + j];
        }
        fixed_type output_value = weighted_sum + bias[i];
        if (output_value < 0) {
            output_value = 0;
        }
        output_stream.write(output_value);
    }
}

// Top-level function for the accelerator
void linear_accelerator(stream_type& input_stream, fixed_type weight[], fixed_type bias[], stream_type& output_stream) {
    #pragma HLS INTERFACE axis port=input_stream
    #pragma HLS INTERFACE axis port=output_stream
    #pragma HLS INTERFACE m_axi port=weight offset=slave bundle=gmem
    #pragma HLS INTERFACE m_axi port=bias offset=slave bundle=gmem

    // Call the linear layer function with RELU nonlinearity
    linear_layer(input_stream, weight, bias, output_stream);
}

Note that we have also updated the function name in the linear_accelerator function to reflect the new implementation with RELU nonlinearity.