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Searched refs:memory_size (Results 1 – 16 of 16) sorted by relevance

/packages/modules/NeuralNetworks/common/cpu_operations/
DSVDF.cpp87 const uint32_t memory_size = SizeOfDimension(weights_time, 1); in Prepare() local
99 stateShape->dimensions = {batch_size, memory_size * num_filters}; in Prepare()
174 const int memory_size = SizeOfDimension(weights_time_, 1); in EvalFloat32() local
176 memcpy(outputStateData, inputStateData, sizeof(float) * batch_size * memory_size * num_filters); in EvalFloat32()
179 float* state_ptr_batch = outputStateData + b * memory_size * num_filters; in EvalFloat32()
181 float* state_ptr = state_ptr_batch + c * memory_size; in EvalFloat32()
182 state_ptr[memory_size - 1] = 0.0; in EvalFloat32()
195 outputStateData[i * memory_size + memory_size - 1] = scratch[i]; in EvalFloat32()
201 float* state_out_ptr_batch = outputStateData + b * memory_size * num_filters; in EvalFloat32()
204 weightsTimeData, state_out_ptr_batch, memory_size, num_filters, scratch_ptr_batch); in EvalFloat32()
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DSVDFTest.cpp168 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, in SVDFOpModel() argument
173 memory_size_(memory_size), in SVDFOpModel()
180 {batches_, memory_size * units_ * rank_}, // state in tensor in SVDFOpModel()
/packages/modules/NeuralNetworks/runtime/test/specs/V1_0/
Dsvdf.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
60 state_in: [0 for _ in range(batches * memory_size * features)],
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_bias_present.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
60 state_in: [0 for _ in range(batches * memory_size * features)],
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf2.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
75 state_in: [0 for _ in range(batches * memory_size * features)],
142 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_state.mod.py20 memory_size = 10 variable
26 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size))
28 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
/packages/modules/NeuralNetworks/runtime/test/specs/V1_2/
Dsvdf_bias_present_float16.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*feature…
60 state_in: [0 for _ in range(batches * memory_size * features)],
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_float16.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*feature…
60 state_in: [0 for _ in range(batches * memory_size * features)],
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_state_float16.mod.py20 memory_size = 10 variable
26 weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (units, memory_size))
28 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*units))
31 state_out = Output("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*units))
/packages/modules/NeuralNetworks/runtime/test/specs/V1_1/
Dsvdf2_relaxed.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
76 state_in: [0 for _ in range(batches * memory_size * features)],
143 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_relaxed.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
61 state_in: [0 for _ in range(batches * memory_size * features)],
128 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_bias_present_relaxed.mod.py22 memory_size = 10 variable
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
61 state_in: [0 for _ in range(batches * memory_size * features)],
128 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
Dsvdf_state_relaxed.mod.py20 memory_size = 10 variable
26 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size))
28 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
/packages/modules/Virtualization/libs/fdtpci/src/
Dlib.rs161 let mut memory_size = 0; in parse_ranges() localVariable
184 && size > memory_size.into() in parse_ranges()
191 memory_size = u32::try_from(size).unwrap(); in parse_ranges()
195 if memory_size == 0 { in parse_ranges()
199 Ok(memory_address..memory_address + memory_size) in parse_ranges()
/packages/modules/NetworkStack/src/com/android/networkstack/metrics/
Dstats.proto250 optional int32 memory_size = 2; field
/packages/modules/NeuralNetworks/tools/api/
Dtypes.spec2418 * get pushed into a memory of fixed-size memory_size.
2419 * * stage 2 performs filtering on the "time" dimension of the memory_size
2460 * A 2-D tensor of shape [num_units, memory_size], where “memory_size”
2465 * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
2476 * [batch_size, (memory_size - 1) * num_units * rank].