DSP Bricklayer: SPmag1990 jan

SPmag 1990 jan

(this post was written while listening to: Dead Europe 72 interleaved with NRPS first album)

We start lucky. In this issue, the main article is a survey of multiplier techniques by the master himself (Fred J. Taylor). Also interesting is the introduction of Matlab version 3.5, including the all new signal processing toolbox. All the book reviewed and many items are relevant to this work. We will NOT finish with a very fashionable topic: Neural Network.

BB struct

The multiplier (fred J. taylor)

This is the DSP BB by excellence. There is hardly any algo which does not rely on a multiplier and it is not cheap. As Fred mentionned, the 16x16 multiplier occupies 40% of the 32020 chip. And I believe 80% of non-memory real estate.

Now, in our world of ideal resources why do we bother with the size of the Mult? Because ideal does not mean infinite. If we develop (say) a 2000 Million node chip, each node would better be optimised. And the best choice for a node (or PE) is a MULT. The next best choice is a DSP core, where MULT is still 80% of the core. (it reminds me of the debate at Xilinx to replace the embedded 18x18MULT with a C25 like core to extend their market share; all people cannot be right all of the time)

So here are the techniques (flattened) given by Fred:

Shit add or iterative
Booth algorithm (and modified~)
Wallaces trees
Cellular arrays ( Perazis, Baugh-Wooley)
Systolic Array Multiplier
Bit Serial
Distributed arithmetic
Canonic signed digit number systems
Logarithmic number systems
Residue Number sytems

With the hindsight of 25 years, what to think? Well it stands pretty good, except for the systolic array.
What about the non-traditional number systems? Frankly we are very NOT positive about these, because the problem is the time lost in the interface.

As for the others, bit serial and DA are common FPGA techniques and are available in Matlab too (how to generate HDL code for a lowpass FIR filter with Distributed Arithmetic (DA) architecture).

The parallel techniques are standard logic techniques but they are still the most promising because the exploration space is very vast.
We have a favorite, which is a kind of subword parallelism. The longest delay in a 16x16 MULT is the final 32-bit adder. This delay can be halved by splitting the adder in 3 16-bit adders. By the same token we can subdivide further with byte and nibble adders hence reducing the delay from 30 to 6 logic gates. We simulated this type of architecture in Matlab (using FP!) (see elsewhere).

Workshop: VLSI for Signal processing at Iccasp90 (Edward Lee)

It is mentionned that several Experimental Parallel Machines for signal processing will be explained. OK let us see.

With the hindsight of 25 years, what to think? A bit of disaster array (sorry area) and we know the results (the GPU). However, in the M2IMP world where the resources are ideal, and the software is straightforward, each of these EPM will be given a good look in due time.

Vector Quantizer (Tran & all, IEEE tr. on com, sep 89)

It is worth asking if the VQ is a structural or functional (speech specific) BB.
In our experience we consider it as a generic unit with app. specific parameters.

Matlab? It is an object in DSP system toolbox .

With the hindsight of 25 years, what to think? This is a good example where Matlab is now the reference.

BB DSP fun

FFT workshop at ICCASP90 (John Cooley)

In this workshop the FFT master explains all the options and innards, including the different radices. Great!

Matlab? FFT is a built-in. The number of samples is totally flexible. The problem is that it is a black box. Not quite all dark as it is based on FFTW. But we had a mixed experience with the golden model methodology. While trying to develop a radix 9 FFT and comparing it to Matlab, stage by stage. As a matter of fact it was easier in Excel. As I am writing this, it does not make sense since in effect I am saying that FFT is non determinstic. But so is my memory of it. I do remember Marc having a lot of trouble with Viterbi decoder but it is different cause because it is a treillis.

Further Transforms - workshop at ICCASP90 (John Cooley)

In this workshop, John goes further with number-theoretic transforms(NTT) and polynomial transforms.

Matlab? As far as I know they are not included. Fortunatly the community supplies them

NTT: NTT.m on matlab central.
PT brought me to the site of chemnitz, Daniel Pots. In turn brough me the NUFFT toolbox.

With the hindsight of 25 years, what to think?

FFT: is now old hand, {see elsewhere}.
NTT; I have no clue, but a NTT engine would be nice.
NUFFT (non uniform FFT) an extremely promising field which I bundled with CS (compressed sampling).

Communication paper: dpll

Matlab? not included; see ecosystem: Modeling and Simulating an All-Digital PhaseLocked Loop By Russell Mohn, Epoch Microelectronics Inc.

Communication paper: sigma delta

Matlab? included

BB in DSP domain

A tutorial at ICASSP 90: SAR (Synthetic Aperture radar) (David Munson)

Matlab? Included, excellent Block diagram and tutorial.

BB in Matlab specific

These Building Blocks are Matlab specific because if they exist it will be more likely in the shape of M-code than anything else. Never met them in DSP software libraries or commercial chips.

Signal processing Toolbox is introduced {advert for Matlab3.5}
First ever SP toolbox. This includes a large number of functions many of which are not very common.

Non Linear Spectral Estimation Methods (Prabhu &all, IEEE proceedings, June 1989)

Here is an article which covers algos than a standard DSP cannot implement. Among these techniques are arma, music and esprit.
Matlab? They can be found over diverse Matlab toolboxes.
Also, many of them can be found in the very professional looking HOSA toolbox which is available from the Central. {HOSA - Higher Order Spectral Analysis Toolbox by Ananthram Swami 12 Feb 2003 (Updated 13 Feb 2003) Spectral and polyspectral analysis, and time-frequency distributions.}

Saturday, November 14, 2015

SPmag1990 jan