## What the Analog FFT (AFFT) can do

The SiliconIntervention AFFT performs three things:

- It acquires the signal (from Lidar or Audio or many other applications) and
- Finds the frequencies present in that signal before presenting the data to the AI or controlling DSP.
- Vastly reduces the data rate of signals going to the AI/DSP so saving weight and power while reducing physical size.

It is valuable because it does these things at **very much lower power** and **hundreds of times faster** than the combination of conventional ADC and AI/DSP can do.

## The Principle of Operation

The Analog FFT computer (AFFT) is a fixed-weight neural network of analog computations that performs a Fourier analysis – it breaks the signal down into its frequency components. It is an example of dynamic aggregate behavior.

It is a neuromorphic analog computer exploiting functionality that emerges from complexity of interconnection: this is what we do at SiliconIntervention. This is a quarter of a million analog devices configured and laid out by code to perform a remarkable and commercially valuable signal processing function.

Way beyond an op-amp or DAC/ADC, this is the new analog in action. It’s the analog design paradigm that works on 2nm processes.

On a digital-only advanced CMOS process (it uses inverter-like amplifiers and needs only MOM or MIM capacitors) the AFFT can calculate a Fourier transform to 128 bins in **<1uS to >70dB SFDR consuming only 900uW**.

The same circuit architecture biased for higher speed can calculate a **128 bin FFT in 2.8nS on a 0.15u process consuming 150mW**. *On 22nm or lower GigaHertz rate FFT calculations can be achieved*.

The same architecture biased for lower power can calculate **Audio Rate (48kFFT/sec) FFTs consuming significantly less than 1 micro-Watt**.

This is helpful because typical data acquisition systems such as voice recognition have a conventional Analog to Digital Convertor (ADC) followed by a digital signal processor (DSP) to find the Fourier transform of that signal.

The electronic circuit is created from code: the well-known Cooley-Tukey Radix 2 Decimation in time algorithm is run with a programmable number of bins (N) and outputs a netlist corresponding to a log2(N) deep layer neural network (NN) each layer having 2N neurons.

The resulting NN may be activated as a round-robin sampler, and since the layers are analog computations, the first layer can directly sample the input signal, replacing the ADC: no ADC is needed to load the NN.

In a breakthrough innovation the SiliconIntervention AFFT architecture can achieve a bin-spreading in the output bins of the AFFT equivalent to an advanced Window Function despite having an equally weighted round-robin input sampler. This means the AFFT can either:

- Sample at mutli-gigahertz speeds for Radio and Lidar applications

-or-

- Achieve very low (nano-Watt levels) in Audio applications

*We are currently developing a complete analog to MFC convertor that performs data sampling, FFT calculation, bin spreading suppression, logarithmic absolute value calculation, summation into a perceptually uniform audio space (MELS), a second FFT of that resulting MELS space (hence a Cepstrum) , encoding into multiple digital outputs at millisecond rates for the AI/DSP and capable of applying a custom interrupt generator from an arbitrary pattern of ceptrum bins; all while consuming tiny micro-watt levels of power.*

**This is by far the lowest power and lowest data rate means to construct an always-on Voice Activity Detector, Pulse Oximeter, or Sound Environment Analysis system.**

Contact us for even more details.

Learn more about static aggregate behavior: an example to find the minimum and maximum of a signal.