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BKK16-100K2 ARM Research - Sensors to Supercomputers
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BKK16-100K2 ARM Research - Sensors to Supercomputers
1.
From Sensors to
Supercomputers Big Data Begins with Little Data Eric Hennenhoefer Linaro Connect 2016 VP Research
2.
©ARM 20162 Introducing ARM
Research About ARM Research 3 – 7 years ahead of product teams From advanced development to blue sky Locations in Austin, Cambridge UK, San Jose, and Shanghai Objectives Build a pipeline to create and bring future technology into ARM products Create and maintain the technology roadmap Enable academia and research partnerships
3.
©ARM 20163 Research Focus
Areas ArchitectureArchitecture • 3D stacked memories • Intent-based interfaces Going Beyond Evolutionary DRAM • Reduce data movement Compute Near Memory • Drive technology • Ensure open standards NVM in the System • Leading future task group Tracking and Driving Memory Roadmaps • TrustZone-M • Improve code density and performance Embedded Efficiency • Super secret stuff • Use transistors more efficiently • Accelerate key use cases Next Gen Arch Security • HW is the root of trust • Make is easier to write secure SW • Novel use cases New Apps Memory & InterconnectMemory & Interconnect
4.
©ARM 20164 Research Focus
Areas Applied SiliconApplied Silicon IoT Sensor Nodes • Sub-threshold for 0.1x energy • Energy optimized mixed-signal • Extreme power gating Integrating everything • Voltage regulators • Energy harvesters • Sensor interfaces Printed Electronics • 1cent disposable MCUs • Mapping the ecosystem Disruptive technology •Next Big Thing Memory •What’s after MOS? •3DIC technology Predictive Technology Modeling •Technology scaling entitlement •Design-Technology Co-Optimization •Next node device, patterning, .. ARM layout on EUV. ASML ISSCC 2013. Dependable Computing •Detection, Correction, Security •Robust power delivery Future Si TechFuture Si Tech
5.
©ARM 20165 Research Focus
Areas Design IntegrityDesign IntegrityLarge Scale SystemsLarge Scale Systems • Improving system efficiency for analytics workloads Data Intensive High Performance Computing • Enable the firstARM supercomputer sideARMs • Compute near memory, network,and storage & standardize systems software interfaces Formal Methods • Formal Coherency Verification on Cortex®-A CPU μArch Models • Verifying implementations against executable spec Rain • Deriving RTL checkers fromArchitecture specification Deadlock Dependency Models • Design-time deadlock freedom for arbitrary interconnect topologies
6.
©ARM 20166 Research Focus
Areas Special ProjectsSpecial Projects Machine Learning • Speech & image recog • Neural networks Graphics Systems • Full-system modeling • System cache arch Computer Vision • Emphasis on automotive • Depth perception, object and motion tracking Mobile Systems • Advanced workloads • HW + SW system design • Future devices ARM motor • Novel motor control Low Power Radio Technology Roadmapping Technical Due Diligence Emerging ApplicationsEmerging Applications
7.
©ARM 20167 What Problem
is IoT Solving? Digital World Physical World IoT
8.
©ARM 20168 The key
is in the connections: Hardware and Software Internet of Things Little Data Big Data Web The Web Things Services Integrated sensors and computing Ultra low power systems
9.
©ARM 20169 Technically,
the eyes, ears, nose, mouth and hands Sensors + compute + connectivity = IoT Sensors are the heart of the IoT Chris Wasden at 2014 MEC, via semiwiki
10.
©ARM 201610 Sense of
Touch: MEMS accelerometer, 3DIC
11.
©ARM 201611 Feature shrinking:
From cell-size to molecule- size 23andMe IMEC / Panasonic
12.
©ARM 201612 Chip senses:
Adding smell and taste Adamant technologies, e.g. http://www.its.caltech.edu/~ahmet/publications.html Food quality, air quality, infectious disease monitoring….
13.
©ARM 201613 Adding hearing
and sight Imaging – thank you cell phone industry From the simple (is it daylight?) To the complex (driving on the highway) Hearing and sight are senses that could be drastically augmented compared to our own physicsworld nature.com crack sensor
14.
©ARM 201614 Sensors Energy
Harvesters will be the heart of the IoT Battery changing or re-charging is not “disappearing into the woodwork” Energy harvesters have low & very variable output power/voltage And slow rate of improvement: ~1%/year for solar Nano-Watt standby allows bottom-end energy storage Charging thin film battery or super-capacitor Minimizing peak power can reduce need for storage Smaller & cheaper device But not always available Theoretical energy density (Source:S.Boisseau,G.Despesse,and B.A.Seddik, “Electrostatic Conversion forVibration Energy Harvesting,” ArXiv e–prints, Oct. 2012.) 300uW @ 5K100uW @ 100Hz Piezoelectric Thermoelectric PV 3mW in direct sunlight 20uW under office lights Sol-Chip Microgen Micropelt
15.
©ARM 201615 Run
a Cortex® -M0 for 10 cycles Write one bit of flash Write ~300 bits of DRAM or SRAM Send ~5 bits across LPDDR4 Transmit 2 bits of UWB data Transmit 0.02 bits over Bluetooth LE Drive an electric car 100fm (@1MJ/km) ~0.05% of the distance across Si atom Energy Efficiency: Things you can do with 100pJ Energy costs to transmit, compute, and store data will define the shape of the IoT VSLI Technology advancements will re-write the boundary conditions Or 1,000,000x / sec with 100 uW
16.
©ARM 201616 180nm Mich
Micro Mote: 30 pJ / cycle Michigan Micro Mote Cortex® -M3 180nm, 8.75 mm3 Vdd = 0.4V, Vt = 0.4V 73 kHz/1 MHz operation G. Chen et al., ISSCC, 2010. Battery Processor Solar Cells http://www.eecs.umich.edu/eecs/about/articles/2015/Worlds- Smallest-Computer-Michigan-Micro-Mote.html
17.
©ARM 201617 65nm M0+
: 11.7 pJ / cycle ISSCC 2015 Heart Monitor Workload 2.3μW3.0μW
18.
©ARM 201618 Big Science
Starts With Little Data Too Transfer antennas to DSP: 200 TB/sRun2: 25GB/s
19.
©ARM 201619 Streaming Data
is the Next Challenge Big Data Graph Streaming 1 Po int query Global sl ice & dice , Data mi ning Fast ingest Fault tolera nce , co de tra nsp are ncy , flexi bility, data re si dence Approved for Unlimited Release: SAND2016-1943 O Reality § Sensors produce enormous quan es of complex data § Current analysis capabili es fail to fully exploit this data to produce ac onable intelligence Challenge: leverage the structure of geospa al data to iden fy pa erns of life § Automated data analysis capabili es that enhance human decision-making § Scalable analysis over disparate temporal and geospa al scales § Pa ern analysis of complex trajectories R&D is required at all levels of the so ware/ hardware stack to automate the capture, fusion, and analy cs of geospa al data streaming from heterogeneous sensors. A convergence of HPC and graph-analy cs is necessary to provide me-sensi ve, ac onable intelligence Geospa al Graph Analysis Approved for Unlimited Release: SAND2016-1943 O 2
20.
©ARM 201620 Even Basic
Little Data can Produce a LOT of Big Data Flavio Bonomi, 2013
21.
©ARM 201621 Dimensions (50cm)3 = 1/8m3 Weight 50kg Power 2kVA Specification (8x) 64
Servers (8x) 256 Cores (8x) 128TB Storage University of Cambridge Portable Cloud ‘Micro’ data centres will dynamically adapt to support storage, web and computation.
22.
©ARM 201622 Connected Teddy
Bears: What Could Wrong? Hackers love IoT If software hacks fail then They will come via UART… The Basics – plan to be hacked Harden the Device Secure boot, Secure kernel, … Perimeter security is insufficient Intrusion detection Deny foothold Revert to known state Secure over-the-air firmware updates Plan to be hacked …
23.
©ARM 201623 Connected Teddy
Bears: What Could Wrong?
24.
©ARM 201624 CCCC AAAACCSS AccelerationAcceleration StorageStorage ComputeCompute AccelerationAcceleration StorageStorage ComputeCompute Packet
Flows Packet Flows AccelerationAcceleration StorageStorage ComputeCompute Packet Flows Devices Edge Data CenterCoreAggregationAccess Applications run where the data is, independent of the network node Heterogeneous Compute is distributed into the network Networks and Compute resources are both managed and configured using standard IT technologies SS AA CCSS CC AA CC AA SS SS AA Scale-Down Power Consumption and Form FactorScale-Down Power Consumption and Form Factor Scale-Up from Little Data to Big Data Decrease LatencyDecrease Latency The Journey From Little Data to Big Data CC
25.
©ARM 201625 Why is
ARM interested in Supercomputing? 43831.0 41426.0 41214.0 41061.0 40848.0 40695.0 40483.0 40331.0 40118.0 39965.0 39753.0 39600.0 39387.0 39234.0 39022.0 38869.0 38657.0 38504.0 38292.0 38139.0 37926.0 37773.0 37561.0 37408.0 37196.0 37043.0 36831.0 36678.0 36465.0 36312.0 36100.0 35947.0 35735.0 35582.0 35370.0 35217.0 35004.0 34851.0 34639.0 34486.0 34274.0 34121.0 32143.0 31048.0 29952.0 27760.0 1.E+05 1.E+06 1.E+07 1.E+08 1.E+09 1.E+10 1.E+11 1.E+12 1.E+13 1.E+14 1.E+15 1.E+16 1.E+17 1.E+18 Supercomputing iPad 2 == Cray 2* * J. Dongarra & P. Luszczek HPEC 2012 Laptop 2014
26.
©ARM 201626 High Performance
Compute (HPC) – Why? Why ARM? - HPC community wants multivendor options Strategic requirement ARM ecosystem brings choice and a path to better optimized solutions Why Now? – Exascale is a compelling event Massive parallelism is requiring changes to software, this opens the door for a new ISA ARM HPC projects are active in multiple regions Why Linaro? HPC has a large open source component Some customers require multiple tools chains: proprietary + open source
27.
©ARM 201627 23%
of HPC system usage is currently HPDA Machine learning Stochastic modeling / Monte Carlo – explore large problem spaces MapReduce/Hadoop, graph analytics, knowledge discovery Many fields benefit from real time results – finance Workloads are migrating to commercial compute servers HPDA – High Performance Data Analysis
28.
©ARM 201628 Deep Learning
– HPC is the Future “This is why around 2008 my group at Stanford started advocating shifting deep learning to GPUs (this was really controversial at that time;but now everyone does it); and I'm now advocating shifting to HPC (High Performance Computing/Supercomputing) tactics for scaling up deep learning.Machine learning should embrace HPC.These methods will make researchers more efficient and help accelerate the progress of our whole field”. Andrew Ng - Quora Feb 3rd 2016
29.
©ARM 201629 HPC Expectations:
Platform Optimized Solutions Machine Learning on ARM example – 80% is about the Math(s)* 1.0x ATLAS from repo is (single core) 2.7x OpenBLAS from repo 6.7x ATLAS self tuned (several hours setup) HPC expectations Easy to access precompiled and optimized packages Scientific packages: Compliers, MPI, math libs, profilers, schedules, pre-build python, … Ability to make power trade-offs Tuned for each silicon vendor and Linux distro Who will lead? OpenHPC? Linaro? *Inference of AlexNet on Caffe with Batch-size 1, 8-core A57
30.
©ARM 201630 ARM HPC
Summary ARM HPC systems are coming, test beds are deployed Tool chains, apps, math libraries, are underway… Open source is a key component of HPC IoT is today and HPDA is a critical piece of the workloads of tomorrow From Sensors to Supercomputers: Big Data Begins with Little Data
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