Use SDRAM to store model_tflite[]

@petewarden

I use the dual core 160 pin microcontroller Arduino Portenta to make TensorFlowLite/Micro models. Recently I found out about an extra 8 MB of SDRAM on the board. I have managed to use the SDRAM to load the 320x320 Grayscale Camera Buffer when using EdgeImpulse.com models, draft code here.

portenta-sdram-3mg

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I would like to put the entire TensorflowMicro model_tflite array into the 8 MB SDRAM, the board normally only has 1 MB of RAM for each core, so using SDRAM would be a fair improvement.

When using SDRAM for the camera frame buffer I was just changing one pointer for another so it was not that hard.

#include <SDRAM.h>

SDRAMClass mySDRAM;

uint8_t *sdram_frame_buffer;



// in the setup

  
  mySDRAM.begin(SDRAM_START_ADDRESS);   // for camera 320x320

  sdram_frame_buffer = (uint8_t *)mySDRAM.malloc(320 * 320 * sizeof(uint8_t));
 

// in the main loop
   int myCamResult =  myCam.grab(sdram_frame_buffer); // myCamResult should be zero 



but for TFLITE I need to work with an array, and that is a bit different when calling the array. Anyone got any suggestions to get me started?

Here is the basic TFLITE code.

const unsigned char model_tflite[] = { ... }

unsigned int model_tflite_len = 3048;


// which is then called using

   model = tflite::GetModel(model_tflite); 

// I have tried passing the array as a pointer (which works in some situations)
// but doesn't seem to work here
// Not really sure what the GetModel method does with the array.

.

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using TFLITE tutorial here
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More info posted on the Arduino Fourm Portenta - Usage of SDRAM.h library - #3 by jerteach - Portenta - Arduino Forum

Just as a quick followup, I chatted to Jeremy elsewhere and think we reached a solution: SDRAM Examples · Issue #38 · arduino/ArduinoCore-mbed · GitHub

Yes, thanks @petewarden the basic SDRAM concept is solved, but when I try to use it with the arduino TensorflowLite HelloWorld (sine wave) it compiles on the arduino Portenta but then gives me the red LED flash of death.

A starting point, I did get the Portenta working using regular TensorflowLite HelloWorld without and SDRAM saving of the Model. Just the arduino_output_handler.cpp file has analogWrite() to the builtin LED which kills the program, since the Portenta Analog pins strangely can’t do PWM.

Changing

analogWrite(led, brightness);

to

digitalWrite(led, brightness);
delay(3);

then works fine (note: it is really fast so the delay helps see the sine wave better).

Then:

The code I was running to test SDRAM is here in the main “hello_world.ino” file

/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <TensorFlowLite.h>

#include "main_functions.h"

#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "constants.h"
#include "model.h"
#include "output_handler.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"


#include <SDRAM.h>
#define ALIGN_PTR(p,a)   ((p & (a-1)) ?(((uintptr_t)p + a) & ~(uintptr_t)(a-1)) : p)
SDRAMClass mySDRAM;




// define SDRAM pointer
alignas(8) unsigned char *sdram_mem;
alignas(8) unsigned char *sdram_tflite; // 32-byte aligned



// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
TfLiteTensor* output = nullptr;
int inference_count = 0;

constexpr int kTensorArenaSize = 2000;
uint8_t tensor_arena[kTensorArenaSize];
}  // namespace

// The name of this function is important for Arduino compatibility.
void setup() {


   mySDRAM.begin(SDRAM_START_ADDRESS);   

   // setup SDRAM memory block
   sdram_mem = (alignas(8)  unsigned char *) SDRAM.malloc(4 + 32 /*alignment*/);
   sdram_tflite = (alignas(8)  unsigned char *)ALIGN_PTR((uintptr_t)sdram_mem, 32);
const int model_tflite_len = 2640;
const int g_model_len = 2640;
//const int g_model_len = 2488;

   memcpy(sdram_tflite, 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model_tflite_len);


  
  // Set up logging. Google style is to avoid globals or statics because of
  // lifetime uncertainty, but since this has a trivial destructor it's okay.
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::MicroErrorReporter micro_error_reporter;
  error_reporter = &micro_error_reporter;

  // Map the model into a usable data structure. This doesn't involve any
  // copying or parsing, it's a very lightweight operation.





  
 // model = tflite::GetModel(g_model);
  model = tflite::GetModel(sdram_tflite);
  if (model->version() != TFLITE_SCHEMA_VERSION) {
    TF_LITE_REPORT_ERROR(error_reporter,
                         "Model provided is schema version %d not equal "
                         "to supported version %d.",
                         model->version(), TFLITE_SCHEMA_VERSION);
    return;
  }

  // This pulls in all the operation implementations we need.
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::AllOpsResolver resolver;

  // Build an interpreter to run the model with.
  static tflite::MicroInterpreter static_interpreter(
      model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
  interpreter = &static_interpreter;

  // Allocate memory from the tensor_arena for the model's tensors.
  TfLiteStatus allocate_status = interpreter->AllocateTensors();
  if (allocate_status != kTfLiteOk) {
    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
    return;
  }

  // Obtain pointers to the model's input and output tensors.
  input = interpreter->input(0);
  output = interpreter->output(0);

  // Keep track of how many inferences we have performed.
  inference_count = 0;
}

// The name of this function is important for Arduino compatibility.
void loop() {
  // Calculate an x value to feed into the model. We compare the current
  // inference_count to the number of inferences per cycle to determine
  // our position within the range of possible x values the model was
  // trained on, and use this to calculate a value.
  float position = static_cast<float>(inference_count) /
                   static_cast<float>(kInferencesPerCycle);
  float x = position * kXrange;

  // Quantize the input from floating-point to integer
  int8_t x_quantized = x / input->params.scale + input->params.zero_point;
  // Place the quantized input in the model's input tensor
  input->data.int8[0] = x_quantized;

  // Run inference, and report any error
  TfLiteStatus invoke_status = interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x: %f\n",
                         static_cast<double>(x));
    return;
  }

  // Obtain the quantized output from model's output tensor
  int8_t y_quantized = output->data.int8[0];
  // Dequantize the output from integer to floating-point
  float y = (y_quantized - output->params.zero_point) * output->params.scale;

  // Output the results. A custom HandleOutput function can be implemented
  // for each supported hardware target.
  HandleOutput(error_reporter, x, y);

  // Increment the inference_counter, and reset it if we have reached
  // the total number per cycle
  inference_count += 1;
  if (inference_count >= kInferencesPerCycle) inference_count = 0;
}


This code compiles, but crashes on startup. Any suggestion to try would be appreciated, but not a huge deal as I have lots of other ML projects on the go.