The document discusses using systems intelligence and artificial intelligence/neural networks to enhance semiconductor electronic design automation (EDA) workflows by collecting telemetry data from EDA jobs and infrastructure and analyzing it using complex event processing, machine learning models, and messaging substrates to provide insights that could optimize EDA pipelines and infrastructure. The approach aims to allow both internal and external augmentation of EDA processes and environments through unsupervised and incremental learning.

1. Providentia Worldwide
S. Ryan Quick @phaedo, Providentia Worldwide. April 2020
HPC Impact
EDA Telemetry Neural Networks

2. Systems Intelligence
Ecosystem Management

3. Providentia Worldwide
Systems
Intelligence
Principles
Methodology for leveraging
multiple data domains
through complex data
processing
Disparate / Unlike Domains
Messaging Middleware
Insight
Insight

4. Providentia Worldwide
• Aggregation
• Event Statistics
• Atomic Pattern Recognition
• Simple example shown as “waterfalling” for
illustration — the operations are parallel and
stateless
• Pattern is an example of the type and method
of telemetry we use for EDA environmental and
in-workload collection to feed AI and neural
networks inline
• There are literally thousands of metrics for a
single operation, millions per job
Multiple-Domain Simple
Data Access
Metrics Calculator
CPU
Event
Source
app login r/sec
app successful login r/sec
app failed login r/sec
cpu 1m load avg
cpu 5m load avg
cpu 15m load avg
cpu blocked proc cnt
cpu running proc cnt
cpu waiting proc cnt
cpu user %
cpu idle %
cpu system %
cpu io wait %
db active queries
db slow queries
db selects
db updates
db deletes
db rows fetched
db table locks held
db row locks held
Available Source Fields App
Login
Event
Source
DB
Access
Event
Source
> 3?
app failed login /
app success
login * 100
AVG(cpu waiting /
cpu running)) / cpu
1M load avg * 100
> 0.5?
DB Slow
Queries
> 4?
Anomaly Detected:
Potential Login
Attack
yes
yes
yes

5. Providentia Worldwide
• Affinity + Simple Case
• Stream + Augmented Datasource
• Parallel Stream
• Frequency-Shifted Stream
• “Correlative/Normalized View”: Similar to a SQL “join”
concept, we relate data fields in disparate stream sources
• Many examples — for other talks :)
• This illustrates the mechanisms by which we can combine
and augment data types for complex events in AI/neural
networks and utilize inline training and active models.
• Also allows us to introduce the notion of insight, which is
crucial to incremental improvement model — especially
for “slight touch ecosystems” like coral reefs
Multiple-Domain Complex
Event Processing
Approaches
Complex Event Processor
CPU
Source
Zookeeper
Source
RabbitMQ
Source
Application
Event
Source
Parallel Source
Disparate
Normalization
Correlative/
Normalized
View
Correlative/
Normalized
View
Correlative/
Normalized
View
approx-data-sz
avg-latency
ephemeral-count
followers
max-fd-cnt
max-latency
min-latency
open-fd-cnt
num-alive-connections
outstanding-requests
packets-received
packets-sent
pending-syncs
synced-followers
watch-cnt
znode-cnt
Zookeeper
message total
message ready
message unasked
rate.publish
rate.deliver
rate.redeliver
rate.confirm
rate.ack
connection.total
connection.idle
channel.total
channel.publisher
channel.consumer
channel.duplex
channel.inactive
exchange.rate.phaedo
q.total
q.idle
q.messages.phaedo
q.consumers.phaedo
q.memory.phaedo
q.ingress.phaedo
q.egress.phaedo
binding.total
RabbitMQ

6. Providentia Worldwide
Semiconductor EDA
Designing the Digital Future

7. Providentia Worldwide
HPC HTC
• “High Throughput Computing”
• Very predictable, common engineering pipeline
• Toolset geared to repeat the steps in the pattern
100s, 1000s of times per iteration, per engineer
constantly. Each adjustment cascades hundreds/
thousands of small jobs.
• Jobs are very short lived. Avg time on single core is
under 3s. Job scheduler itself is often a
bottleneck on large, shared systems.
• EDA requires multiple phases of HDL synthesizers
and HLL compilers and so can result in different
sorts of computational bottlenecks at different
phases of the pipeline as well as resulting for
different design choices in the engineering
decisions.
EDA Characteristics

8. Providentia Worldwide
Well-established Sector
• Traditional enterprise storage (NFS3)
• 10-100M small <=1M files/dir)
• user and group based access controls
• POSIX, locking not required
• OS scheduler is often sufficient. Sometimes,
job submission separated by login node.
• License model well understood, and generally
by core or time-based. Codes are generally
proprietary.
• Turnkey deployment is up and running in
minutes on nearly any sized system. Very little
motivation to alter the status quo.
EDA Characteristics

9. Providentia Worldwide
What Would it Take to Try something new?
• All on-prem, w/ cloud tests successful
but not adopted:
• too costly
• intellectual property concerns
• ROI delayed
• data management difficulties
• Storage enhancements show
improvements, and large shops adopt
those, but NFS3 performs well for
most small-medium practitioners.
EDA Environments

10. Providentia Worldwide
What Would it Take to Try something new?
• EDA Process is well-known, easy-to-
hire to, and well-understood in the
industry. Why rock the boat?
• Any perturbations to the system
would need to overcome the cost of
change, which in semiconductor
fabrication can be immense.
• Even where bottlenecks are known
(storage, compute, scheduling), they
are understood and manageable.
New is new and unpredictable with
unknown value…
EDA Pipelines at Scale?

11. Providentia Worldwide
For valuable and motivational change in
semiconductor EDA, we need disruption both
in behavior and environment simultaneously.

12. Providentia Worldwide
External focus for HTC/Systems Intelligence
• Two primary mechanisms for
augmenting the EDA process:
Internally (inside the EDA
pipeline).
Externally (augmenting and
enhancing the pipelining
environment).
We are focusing here for this
project, but the usual neural
network caveats apply.
Neural Networks for EDA Pipelines
Semiconductor Electronic Design Automation
«precondition» API to workflow data
Chip Specification
Design entry/Functional verification
RTL synthesis
Partitioning of chip
Design for test (DFT) insertion
Floor planning
Placement stage
Clock tree synthesis (CTS)
Routing stage
Final verification
GDS II
Infrastructure Automation
«precondition» API to all components
«precondition» API backwards compatible
Systems Provisioning
Network Provisioning
Application Deployment
Configuration Management
Platform Management
Change Orchestration
capabilities
XY
User/group file CRUD
Workflow scheduling
Job management
License management
sd Systems Intelligence — EDA Messaging Substrate
Data Analytics Command & Control
Internal
External

13. Providentia Worldwide
Semiconductor EDA
Designing the Digital Future
“When we think of sensing technologies as devices
that order the world, rather than devices that describe
it, then alternative relationships between the social and
the technical are strikingly brought to light.”
— Genevieve Bell (Intel) @feraldata

14. Providentia Worldwide
EDA Workflow and Supporting Infrastructure SI Messaging
XY
User/group file CRUD
Workflow scheduling
Job management
License management
X
Y
sd Systems Intelligence — EDA Messaging Substrate
C
E
P
I
n
g
e
s
t
Data Analytics
inline models
offline models
Atomic Pattern
Recognition
Parallel Stream
Command & Control
Stream Augmentation
data/scores/metrics
decisioning
orchestration
validation
feedback
Frequency-Shifted
Streams
Affinity Streams
Aggregation/ Statistics
Semiconductor Electronic Design Automation
«precondition» API to workflow data
Chip Specification
Design entry/Functional verification
RTL synthesis
Partitioning of chip
Design for test (DFT) insertion
Floor planning
Placement stage
Clock tree synthesis (CTS)
Routing stage
Final verification
GDS II
Infrastructure Automation
«precondition» API to all components
«precondition» API backwards compatible
Systems Provisioning
Network Provisioning
Application Deployment
Configuration Management
Platform Management
Change Orchestration
capabilities
XY
User/group file CRUD
Workflow scheduling
Job management
License management
X
Y
sd Systems Intelligence — EDA Messaging Substrate
C
E
P
I
n
Data Analytics
inline models
offline models
Atomic Pattern
Recognition
Command & Control
Stream Augmentation
data/scores/metrics
decisioning
orchestration
External Capabilities and Infrastructure
EDA SI Messaging Substrate
Insight
Insight

15. Providentia Worldwide
EDA Workflow and AI/NN Frameworks
Semiconductor Electronic Design Automation
«precondition» API to workflow data
Chip Specification
Design entry/Functional verification
RTL synthesis
Partitioning of chip
Design for test (DFT) insertion
Floor planning
Placement stage
Clock tree synthesis (CTS)
Routing stage
Final verification
GDS II
Infrastructure Automation
«precondition» API to all components
«precondition» API backwards compatible
Systems Provisioning
Network Provisioning
Application Deployment
Configuration Management
Platform Management
Change Orchestration
capabilities
XY
User/group file CRUD
Workflow scheduling
Job management
License management
X
Y
sd Systems Intelligence — EDA Messaging Substrate
C
E
P
I
n
Data Analytics
inline models
offline models
Atomic Pattern
Recognition
Command & Control
Stream Augmentation
data/scores/metrics
decisioning
orchestration
GDS II
XY
User/group file CRUD
Workflow scheduling
Job management
License management
sd Neural Networks
sd Messaging-Based Machine Learning / AI / Neural Networks Workflow
Data Analytics and
Normalization
Reactive Systems
scoring/metrics
decisioning
orchestration
validation
feedback
inline learning models
Clustering,
Classification, Decision
Trees
Insight
Consumers
Ecosystem Insight and
KPI Enhancements
Ecosystem Messaging Platform
Pattern Enhancements
ModelRunModelTraining
Offline / replay learning models
CEP/INGESTfromExisting
Datasources
X
Y
Y
X
External Capabilities and Infrastructure
EDA ML / AI / NN Workflow
SIMessagingSubstrate
Insight
Insight
Insight

16. Providentia Worldwide
Unique position for AI and NN
Why Artificial Intelligence/Neural Networks for this Problem?
• Small, incremental human-driven changes are not cost-effective in
today’s DevOps systems
• Continuous observation for “minority report” style changes is difficult
to design sprints and test efficacy, even harder to measure ROI
• Command and control systems can be designed to allow incremental
change directly from NNs based on deployments — e.g. allow each
“reef” to tune itself based on its own ecosystem
• The “show your work”/“show your rationale” problems are weaker in
EDA compared to delivering results than in other domains

17. Providentia Worldwide
Insight: “looking inward”
Insight provides a mechanism for self-tuning behavior of the running system at all
levels:
•algorithms, models, data access, expert systems, KPIs, behaviors, reports,
accuracy, efficiency, even insight itself
•In-built feedback mechanism for capturing behavior and performance
•Mechanism to ensure that changes over time are accounted for and noticed if not
understood
•Allows for inline and ongoing training without having to maintain offline (and
outdated) training datasets
•Allows for locale-specific NN training (the NN-locale problem).

18. Providentia Worldwide
Program Status
Where are we now?
• Telemetry data from workload systems feeding messaging platform
• Synthetic workload (provided from partner benchmarking suite) being modified for user-
emulation
• NN specific topology choice and models under discussion with wider team considering
we will need to utilize simultaneous learning, model promotion, results propagation, etc.
• Insight mechanisms are developed in the messaging substrate automatically, with
common APIs available to higher level structures. Common reporting in dashboards etc.
• Always looking for helpers to take things farther — will report more later as we
(un)shelter…