Neural Networks with Cellular Automata

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 1 /39

SEASONs:Spiking, Entropic, Asynchronous,Self-Organizing Neural Networks

On self-modifying ‘machine learning’ systems

Ph.D. project: Ludovic A. Krundel

Supervisors : Dr. David J. Mulvaney

Dr. Vassilios A. Chouliaras

Neural Networks with Neural Networks with Cellular AutomataCellular Automata

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 2 /39

Theme (talk emphasis)

● Innovative Emergence

● Connectionist approach

● Bottom-Up engineering (vs. conventional Top-Down engineering)

● Self-Modifying systems

● examples of physical implementation of NN on hardware device

● Aspects of the work:

– Scalability of NN

No learning algorithm-ready for more than 100s neurons

Interconnects issues

– Incremental Learning: trial-and-error processes ( no learning without errors)

– Asynchronous operation of the microelectronic system

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 3 /39

CPU: ubiquitous

●established industrialised solution as general purpose processor

●Typical example of full Top-Down design engineering

●sequencer

●low software level @ high hardware level

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 4 /39

CPU: insight

introduction

Sequencer(Automaton)Sequencer(Automaton)

Processing Unit Memory

Arithmetic and

Logic Unit

Instruction Register RI

Exchange Unit

Accus

Exchange Register RE

Address Register RAM

Ordinal Counter CO

Data

or

Instruction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 5 /39

CPU: the good(?) old stuff?

● fatal processing error (e.g. wrong branching)

– not reliable for critical real-time system applications

embedded software for real-time applications: aircraft autopilot, space devices

microcontrollers for medical systems

● (exhibits) untrue time-parallelism: high surface/data-time-processing cost

– Instruction level parallelism

Pipelining

Superscalar design

– Thread level parallelism (MIMD)

Multiprocessing

Multithreading

– Data parallelism (SIMD) multimedia applications

Vector processor (vectors of data)

introduction

superscalar

pipeline

subscalar

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 6 /39

Manufacture of CPU

ASIC design flow

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 7 /39

Manufacture of CPU

Photolithography of ASIC circuit

CMOS Technology

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 8 /39

FPGA (Field-Programmable Gate Array)

●“Programmable Chip”

(Re-)Configurable Microelectronic Circuit

●True Time-Parallelism

●Massively parallel, highly distributed

processing units as in ASIC

●Examples of application: ASIC prototyping,

upgradeable hardware device, labs experiments

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 9 /39

FPGA: insight

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 10 /39

FPGA: manufacture

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 11 /39

SOC (System-On-a-Chip)

●True Time-Parallelism + CPU

●Lower surface/data-time-processing costs

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 12 /39

SOC: examples of application

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 13 /39

SOC: state-of-the-art

●Embedded FPGA (eFPGA): www.menta.fr

●Embedded CPU (eCPU): Mitrionics

– Massively parallel eCPU on FPGA

Supercomputing performance

introduction

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 14 /39

Conventional CAM (Cellular Automata Machine):“CA are a natural way of studying the evolution

of large physical systems and constitute a general paradigm for parallel computation” [Toffoli and Margolus, 1987]

● Clocked: whole 2D CA landscape updated at each clock tick (nth-order systems/rules)

● Two-state cells: e.g., alive or dead [Conway’s “Game of life”]

● Local (action-at-a-distance forbidden) and uniform (single rules’ recipe) system’s rules

part 1

Pure CA (Cellular Automata)

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 15 /39

Examples of self-organizing CAs

Innovative Emergence?

In big CA systems, complex global behaviours can emerge! [S. Wolfram, 2002]

Neuron-like CAs:

Dendritic CA

part 1

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 16 /39

NNs with CA

●CA can be configured to create functions

●Non-Linear Dynamics and Chaos

– Organized complexity innovative emergence

●Substrate for highly adaptive NNs

part 1

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 17 /39

EHW

●Intrinsic vs. Extrinsic EHW

●Evolving Neural Networks based on

Cellular Automata in hardware

●Genetic Algorithms

part 1

bit-streamJTAG

chromosome

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 18 /39

The CBM

●The CAM Brain Machine [H. de Garis, 1999]

●CAM = Cellular Automata Machine

●Growing CA-based NN modules for the

behaviour of a robot cat: Robokoneko

●Too many rules

Need ‘Rule Learning’

part 1

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 19 /39

CNN: Cellular Neural Networks

part 1

●CNN for Machine Vision [Pr. Leong Chua]

●POEtic embryonic tissue

– Phylogenesis: evolution

– Ontogenesis: development

– Epigenesis: learning

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 20 /39

CNN: POEtic embryonic tissue

part 1

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 21 /39

CA-based NN

●Evolved Neural Networks based on

Cellular Automata for sensory-motor

controller [Kim & Cho, 2005]

part 1

Khepera

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 22 /39

Cell Matrix

●The PIG Paradigm (Nicholas Macias, 1999)

●disappeared in 2005?

●FPGAs can actually also do it:

– ICAP: internal configuration access port

– Dynamic-reconfiguration efforts (IPFlex)

– Partial-reconfiguration efforts (JBits)

– Self-reconfiguration efforts (endo-reconfig)

part 1

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 23 /39

Some Interesting Features of NNs

●Rules Extraction (from non-linear

systems) – Inductive Inference

●Learning and automatic generation of

rules instead of evolutionary approaches

●Cognitive learning vs. blind learning

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 24 /39

Entropy Comparison Engine

●Improvability Evaluation: Self-Upgrades

●Similar concept to CPU sequencer:

systematic core engine

●Comparing NN system entropy with

environmental information

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 25 /39

Learning Algorithm

●Robot Controller: sensor-activator

– Sensor Signals Fusion

●Unsupervised Learning

●Spiking Neurons

●Self-Adaptive NN

– Entropy-based NN re-organization

– Automatic generation of CA rules

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 26 /39

Scalability

● Interconnects issues:

– NOC (Network-On-a-Chip)

– But FPGAs are now dominated by interconnects, just like NNs

●For more than 100s neurons:

– Modular architecture

multiplicity of learning algorithms?

– Hierarchical architecture

pseudo-fractal architecture?

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 27 /39

Ongoing Work: Design Approach

1. Top-down design and debug of one cell

– Using conventional EDA tools and design flows

– Implement at hand a minimal set of initial rules: basic rules

2. Bottom-up assembly as multiple instantiations of totipotent cells in a modular-hierarchical fashion

– Using HDL (Balsa, Verilog) parameterization facilities:

recursive instantiation

instantiation by name

3. Real-Time Simulations of the global hardware system

– Parametric simulations

– Comprehensive analyses

– Goto 1.: Eventual refinements of the cell design and initial rules

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 28 /39

Cell Architecture

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 29 /39

Possible Cell Structure

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 30 /39

Self-configured CA-based NN

Bottom-Up Engineering

part 2

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 31 /39

Selected FPGA design flow

part 3

Xilinx ISE

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 32 /39

Simulation with Balsa

●Spiking Entropic Asynchronous Self-

Organizing Neural networks: SEASONs

●Balsa development and simulation

environment

part 3

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 33 /39

Simulation with Balsa

●Balsa simulation environment

part 3

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 34 /39

FPGA Development Board

part 3

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 35 /39

Other Hardware Machine-Learning

Techniques for Self-Modifying Systems

1. Use SystemC or Handel-C and write synthesizable(?) self-modifying code

2. Perform software simulations

3. Target hardware FPGA

4. Perform hardware simulations and compare

discussion

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 36 /39

Neuropeptides

●nervous system + hormonal system

(immune system)

discussion

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 37 /39

Neuro-Modulation

●why hardware machine-learning

discussion

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 38 /39

Main Points

● SOC

● ASIC / FPGA

● CPU

● Digital Spiking NN based on asynchronous CA that dynamically reconfigure themselves to suit the changing task at hand

● Aware learning vs. blind adaptation

– Systematic learning mechanisms should not dominate a machine-learning technique

● Innovative Emergence of self-adaptation

conclusion

● CAM

● SEASON

Self-Adaptive Hardware Machine-Learning Systems Ludovic Krundel

Apr 8, 2023 39 /39

Acronyms

● FPGA: Field-Programmable Gate Array

● CPLD: Complex Programmable Logic Device

● ASIC: Application Specific Integrated Circuit

● SOC: System-On-a-Chip

● FSM: Finite State Machine

● CPU: Central Processing Unit

● DSP: Digital Signal Processor

● NN: Neural Network

● CA: Cellular Automata

● COTS: Commercially Off-The-Shelf

● EHW: Evolvable HardWare

● CAM: Cellular Automata Machine

● CBM: CAM Brain Machine

● JTAG: Joint Test Action Group

● ICAP: Internal Configuration Access Port

● RTL: Register Transfer Level

● EDIF: Electronic Design Interchange Format

● HDL: Hardware Description Language

● EDA: Electronic Design Automation

● VLSI: Very Large Scale Integration

● CMOS: Complementary Metal Oxide

Semiconductor