How can you optimize DSP algorithms for minimal memory usage?

1 Choose the right data types

One of the simplest ways to reduce memory usage is to choose the appropriate data types for your DSP variables and operations. Depending on the precision and range of the values you need to represent, you can use different types of integers, fixed-point, or floating-point numbers. For example, if you only need to store values between -1 and 1, you can use a 16-bit fixed-point number instead of a 32-bit floating-point number, saving half of the memory space. You can also use smaller data types for intermediate calculations, as long as you avoid overflow and underflow errors.

2 Use memory-efficient data structures

Another way to optimize memory usage is to use data structures that are suitable for your DSP algorithm and data. For example, if you need to store a sparse matrix, you can use a compressed format that only stores the non-zero elements and their indices, instead of a full matrix that wastes memory on zeros. Similarly, if you need to store a sequence of values that have a regular pattern, you can use a run-length encoding that stores the value and the number of repetitions, instead of storing each value separately.

3 Exploit symmetry and redundancy

Many DSP algorithms and data have some form of symmetry or redundancy that can be exploited to save memory. For example, if you need to perform a Fourier transform on a real-valued signal, you can use a half-size complex-valued array to store the frequency domain data, since the other half is just the complex conjugate of the first half. Likewise, if you need to apply a convolution or a correlation on a signal, you can use a circular buffer that reuses the same memory locations for the input and output data, instead of allocating separate arrays.

4 Use in-place operations and reuse memory

Another technique to optimize memory usage is to use in-place operations and reuse memory whenever possible. In-place operations are those that overwrite the input data with the output data, without creating a new copy of the data. For example, if you need to apply a filter on a signal, you can use an in-place filter that updates the signal values as it reads them, instead of creating a new filtered signal array. Reusing memory means that you use the same memory locations for different variables or operations, as long as they do not interfere with each other. For example, if you need to perform multiple matrix multiplications, you can use the same temporary array for storing the intermediate results, instead of allocating a new array for each multiplication.

5 Apply algorithm-specific optimizations

Finally, you can apply some optimizations that are specific to your DSP algorithm and its implementation. For example, if you need to perform a discrete cosine transform (DCT) on an image, you can use a fast algorithm that exploits the symmetry and separability of the DCT, reducing the number of computations and memory accesses. Similarly, if you need to implement a finite impulse response (FIR) filter, you can use a polyphase structure that decomposes the filter into sub-filters, reducing the memory requirements and improving the performance.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?