//
// This confidential and proprietary software may be used only as
// authorised by a licensing agreement from ARM Limited
// (C) COPYRIGHT 2020 ARM Limited
// ALL RIGHTS RESERVED
// The entire notice above must be reproduced on all authorised
// copies and copies may only be made to the extent permitted
// by a licensing agreement from ARM Limited.


=== Activation Functions

==== CLAMP
Clamp to an arbitrary minimum and maximum value. Note that the maximum and minimum values are specified as signed quantized values, no scaling happens before or after this operation.

*Arguments:*

|===
|Argument|Type|Name|Shape|Description

|Input|in_t*|Input|shape|Input tensor with rank from 1 to 4
|Attribute|in_t|min_val|-|minimum clip value
|Attribute|in_t|max_val|-|maximum clip value
|Output|out_t*|Output|shape|Output tensor of same type and shape as input
|===

*Operation Function:*
....
assert(rank(shape)<=4)
for_each (index in shape) {
    value = tensor_read<in_t>(input, shape, index)
    acc = apply_clip(value, min_val, max_val)
    tensor_write<out_t>(output, shape, index, acc)
}
....

*Supported Data Types:*

|===
|Profile|Mode|in_t|out_t

|Any|signed 8|aint8 |aint8
|Any|signed 16|int16|int16
|MI, MT|float|float|float
|===

==== RELUN

ReLU with a scalar maximum value.

*Arguments:*

|===
|Argument|Type|Name|Shape|Description

|Input|in_t*|Input|shape|Input tensor with rank from 1 to 4
|Attribute|in_t|max_val|-|maximum clip value
|Output|out_t*|Output|shape|Output tensor of same type and shape as input
|===

*Operation Function:*

[source,c]
----
for_each (index in shape) {
    in_t value = tensor_read<in_t>(input, shape, index)
    acc = apply_clip<in_t>(value, 0, max_val)
    tensor_write<in_t>(output, shape, index, acc)
}
----

*Supported Data Types:*

|===
|Profile|Mode|in_t

|Any|signed 32|int32
|MI, MT|float|float
|===

==== SIGMOID

Sigmoid function: output = 1 / (1 + exp(-input))

For quantized integer data types, the TABLE operator should be used instead with
the following definition.

The sigmoid table has 513 entries each of 16-bit precision and covering the input range -16.0 to +16.0 in steps of 1/16.

[source,c]
....
int sigmoid_reference(int x) {|// input x range is -256 to + 256 inclusive
    F64 v = (double)x/(double)16;
    v = 1.0/(1.0+exp(-v));
    return round_to_nearest(32768.0 * v);
}

generate_lookup_table(&sigmoid_table, &sigmoid_reference);
....

*Arguments:*

|===
|Argument|Type|Name|Shape|Description

|Input|in_t*|Input|shape|Input tensor with rank from 1 to 4
|Output|out_t*|Output|shape|Output tensor of same type and shape as input
|===

*Supported Data Types:*

|===
|Profile|Mode|in_t|out_t

|MI, MT|float|float|float
|===

==== TANH

Parameterized hyperbolic tangent.

For quantized integer data types, the TABLE operator should be used instead with
the following definition.

The tanh_table has 513 entries each of 16-bit precision and covering the input range -8.0 to +8.0 in steps of 1/32. The table is specified by:

[source,c]
----
int tanh_reference(int x) {  // input x range is -256 to +256 inclusive
    F64 v = (double)x/(double)32;
    v = exp(-2.0*v);
    v = (1.0-v)/(1.0+v);
    return round_to_nearest(32768.0 * v);
}

generate_lookup_table(&tanh_table, &tanh_reference);
----

*Arguments:*

|===

|Argument|Type|Name|Shape|Description

|Input|in_t*|Input|shape|Input tensor with rank from 1 to 4
|Output|out_t*|Output|shape|Output tensor of same type and shape as input
|===

*Supported Data Types:*

|===

|Profile|Mode|in_t|out_t

|MI, MT|float|float|float
|===