New DSP Instructions in the Pico 2

Prompt

What are the new DSP assembly language functions in the Raspberry Pi Pico 2 RP2350 instruction set ARMv8-M + Thumb-2. How can they be used to speed up a FFT algorithm

Note

As of March 22nd, 2025 when this was written, OpenAI's ChatGPT o1 knowledge cutoff date is June 2024. The Pico 2 was introduced on Aug 8, 2024. So OpenAi is not aware of the Pico 2 announcement and its features. This is why I used Perplexity since it is able to search the current internet as sources for its results.

Perplexity Response

The new Raspberry Pi Pico 2’s RP2350 microcontroller introduces enhanced DSP capabilities through its ARMv8-M architecture and Thumb-2 instruction set, enabling significant performance improvements for FFT algorithms. Here’s how these features are leveraged.

Key DSP Assembly Language Functions

1. Floating-Point Unit (FPU) Integration

The RP2350 includes a dedicated FPU, allowing hardware-accelerated floating-point operations critical for FFTs. This replaces slower software-based emulation used in the earlier RP2040, reducing computation time for operations like complex multiplications and additions²³.

2. Thumb-2 Instruction Set

Thumb-2 combines 16- and 32-bit instructions, improving code density and execution efficiency. For example:

; Example: Multiply-Accumulate (MAC) operation using FPU
VMLA.F32 S0, S1, S2  ; S0 = S0 + (S1 * S2)

This single-cycle MAC instruction accelerates FFT’s butterfly operations¹⁴.

3. DSP Extensions

ARMv8-M adds DSP-oriented instructions like SIMD (Single Instruction Multiple Data) for parallel processing. For instance:

; SIMD example for parallel data processing
VADD.F32 Q0, Q1, Q2  ; Add four 32-bit floats in Q1 and Q2, store in Q0

This reduces loop overhead in FFT stages by processing multiple data points simultaneously²⁴.

Optimizing FFT with RP2350 Assembly

1. Hardware-Accelerated Arithmetic

Replace software-based floating-point operations with FPU instructions.
Use VFP registers (S0-S31) for intermediate FFT results to avoid memory bottlenecks³⁵.

2. Loop Unrolling and Register Management

Unroll FFT butterfly loops in assembly to minimize branch penalties. Prioritize registers R0-R7 (Thumb-2’s low registers) for frequent operations¹⁶.
Example:

; Unrolled loop for FFT stage
.thumb_func
fft_stage:
    VLD1.F32 {S0-S3}, [R0]!  ; Load 4 floats
    VADD.F32 S4, S0, S1      ; Compute real part
    VSUB.F32 S5, S0, S1      ; Compute imaginary part
    VST1.F32 {S4-S5}, [R1]!  ; Store results

3. Memory Optimization

Use DMA channels to asynchronously transfer audio data from I2S peripherals (e.g., microphones) to RAM, freeing the CPU for FFT computations³⁵.

Performance Impact

A 256-point FFT on the RP2350 completes in 17 ms using optimized ARM assembly, compared to ~50 ms on the RP2040³. Key metrics:

FPU Utilization: Reduces floating-point operation latency by 4–10×lessons.
SIMD Parallelism: Processes 2–4 data points per instruction, cutting FFT stage time by up to 50%⁵.

For real-time audio applications (e.g., spectrum analyzers), this allows:

8 kHz sampling with 256-point FFTs at 13.1 FPS³.
Lower latency for effects like reverb or noise suppression⁵.

Implementation Steps

Toolchain Setup

Use the pico-sdk with .thumb_func directives to ensure Thumb-2 compatibility¹.

Leverage Existing Libraries

Adapt ARM-optimized FFT libraries (e.g., Peter Hinch’s FFT library) to exploit RP2350-specific instructions³⁵.

Profiling

Use cycle-counting tools to identify bottlenecks in FFT stages and replace them with inline assembly⁶.

By integrating these techniques, developers can achieve real-time FFT performance on the Pico 2 for audio processing, spectral analysis, and IoT applications.

Footnotes

Other References

Answer from Perplexity: pplx.ai/share