HiPEAC, Bologna – January 2020

CERBERO: Challenges and Solutions

Self-Adaptation

Motivation: Systems such as Planetary Exploration Use Case

Self-Healing System for Planetary Exploration: • WHAT: FPGA based Robotic arm controller. • NEEDS: Self-healing and self-adaptive capabilities to adapt to harsh

environmental conditions

Tool Integration

Early Stage Design Space Exploration &

Components Deployment

Run-Time Computing Level

Adaptation

Multi-Objective Run-Time Optimisation

Increase productivity

Performance, energy, reliability, …

Need of lightweight tools and models

Triggers for Adaptation

ENVIRONMENTAL AWARENESS: Influence of the environment on the system. Sensors.

USER/EXTERNALLY-COMMANDED: System-User interaction. Human-machine interfaces.

SELF-AWARENESS: Internal status variation. Status monitors.

Planetary Exploration

Current arm position. Known obstacles.

Unknown obstacles.

Final position of the robotic arm.

Performance monitors (i.e. throughput).

Fault monitors. Consumed Energy.

Command Adaptation: Put in place the actions for the required adaptation

Estimate Performance/quality/ energy: runtime models

Analyse & Decide: Change task – Optimize - Repair

Formalization based on adaptation loop components

Sense the world (PHY): Context awareness Sense the system (CYB): Self-awareness

Sense and monitor

Estimate KPI

Decide to adapt

Produce adaptation…

… on an adaptable

Fabric

Adapt: Reconfigure the heterogenous and multi-level computing infrastructure. Multiple fabrics.

… but CERBERO style, and multi-layer

CYB runtime models (energy, performance)

1. Spider – (SW + MDC + A3) 2. Evolutionary JIT HW 3. Spider – Apollo

Self-adaptation loop in a nutshell. CERBERO Options

Papify (embedded) SW and HW PAPI components consistently used

Monitors

KPI estimation

Decide to adapt

Produce adaptation…

Adaptation Fabrics

1. SW components 2. MDC components 3. ARTICo3 components 4. Overlays for JIT HW composition

1. DPR (block, fine grain) 2. Virtual (mux-based) 3. LUT-based reconf. 4. Apollo – Polyhedral transformations

Design time /run-time tools for all fabrics

PREESM

SW

MDC

ARTICo3

Overlays

Design time exploration

ARTICo3 toolflow

MDC toolflow

IMPRESS

Spider

Run time adaptation

Spider MDC

Spider A3 runtime

Coarse grain

Scalability & fault tolerance

Fabric types

HW design

Deterministic

PAPIFY

Estimated KPIs

HW Overlay use cases

Evolutionary HW (bbNN)

Mixed-grain

PAPIFY

SW design

Polyhedral transf.

Tool support for Multi-Grain Adaptivity (ARTICo3 + MDC)

N:1

Tool support for Multi-Grain Adaptivity (ARTICo3 + MDC)

Tool support for Multi-Grain Adaptivity (ARTICo3 + MDC)

• WHO:

• SPIDER

• WHAT:

• Deploy applications in HW and SW fabrics on the fly on the available resources.

• WHY:

• Functional & Non-Functional Needs

• HOW:

• Parameterized Dataflow MoCs

• KPI Runtime Models

• Model of the Architecture

SPIDER

Evolutionary algorithm

BBNN Genetic

algorithm

Evolutionary JIT HW composition