For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/crossbar/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Sylvain Dubois, Vice President of Business Development and Marketing at Crossbar, presents the "Memory-centric Hardware Acceleration for Machine Intelligence" tutorial at the May 2019 Embedded Vision Summit. Even the most advanced AI chip architectures suffer from performance and energy efficiency limitations caused by the memory bottleneck between computing cores and data. Most state-of-the-art CPUs, GPUs, TPUs and other neural network hardware accelerators are limited by the latency, bandwidth and energy consumed to access data through multiple layers of power-hungry and expensive on-chip caches and external DRAMs. Near-memory computing, based on emerging nonvolatile memory technologies, enables a new range of performance and energy efficiency for machine intelligence. In this presentation, Dubois introduces innovative and affordable near-memory processing architectures for computer vision and voice recognition, and presents architectural recommendations for edge computing and cloud servers. He also discusses how nonvolatile memory technologies, such as Crossbar Inc.’s ReRAM, can be directly integrated on-chip with dedicated processing cores, enabling new memory-centric computing architectures. The superior characteristics of ReRAM over legacy nonvolatile memory technologies help to address the performance and energy efficiency demands of machine intelligence at the edge and in the data center.

1. © 2019 CROSSBAR - www.crossbar-inc.com
Memory-centric Hardware Acceleration
for Machine Intelligence
Sylvain Dubois – VP Business Development
CROSSBAR
May 22nd,2019

2. © 2019 CROSSBAR - www.crossbar-inc.com
• Uploading, processing and downloading
from cloud takes time
• Transmitting data burns energy
• Some apps cannot rely on wireless
connection
• Data less exposed if processed locally
BATTERY LIFE
PERFORMANCE
RELIABILITY
SECURITY & PRIVACY
>37B IoT semiconductor chips in 2018
CLOUD
EDGE
Pervasive Machine Intelligence to the EDGE
2

3. © 2019 CROSSBAR - www.crossbar-inc.com
The Challenge of AI
Moving data takes time and burns energy
3
• In-processor Crossbar ReRAM: <0.5pJ/bit
• In-processor SRAM:
• 6pJ/bit for 8Mbit → 47pJ/bit for 64Mbit
• In-package HBM DRAM: 64pJ/Byte
• DDR4 DIMMs: 320 pJ/Byte
COMPUTING
(volatile)
BIG DATA
(non-volatile)
GAP
Solution: Bring data closer to compute
Read latency Read energy
AI is about energy-efficient data access

4. © 2019 CROSSBAR - www.crossbar-inc.com
Memory Technologies Trends
4
DRAM DRAM-DDR
SRAM SRAM
MRAM
MRAM-F
ReRAM
ReRAM(1T1R)
PCM PCM (3D-XP)
NAND 3D-NAND (QLC)
HBM
3D-SRAM
MRAM-S
3D-ReRAM(1TnR)
STORAGE
STORAGE CLASS MEMORY
(SCM)
EMBEDDED MEMORY
MEMORY
VOLATILENON-VOLATILE
RD & WR sequential perf
(latency/bandwidth)
Unlimited RD&WR cycling
Cost-effective CMOS
integration and silicon area
Lower energy / bit than
external memory
Better performance than
external memory
RD perf between DRAM
and NAND
Cheaper than DRAM cost
per GB
Lowest possible cost per GB
Driving factorsToday … near future

5. © 2019 CROSSBAR - www.crossbar-inc.com
Resistive RAM (ReRAM) Fundamental Technology
5
Program
Reading
a programmed cell Erase
Reading
an erased cell
LOW
RESISTANCE
(ON)
HIGH
RESISTANCE
(OFF)
Non-volatile way to store information using nano-filaments
Integrated in
standard CMOS
back-end
Top Electrode
Switching Medium
Bottom Electrode

6. © 2019 CROSSBAR - www.crossbar-inc.com
Crossbar Selector Technology
6
1T1R Selector
+ ➔
active
off
‘1’
‘0’
‘1’
‘0’
off
High noise-suppression ratio
(> 1 million) with sharp transition
High performance NVM
High performance & high density NVM
> 106 x 1TnR
active

7. © 2019 CROSSBAR - www.crossbar-inc.com
Leading Embedded Memory Technologies
7
STT-MRAM-F (FLASH-LIKE) STT-MRAM-S (SRAM-LIKE) Crossbar ReRAM
Physical Mechanism
Spin-polarized current
1.5 ON/OFF ratio
Spin-polarized current
1.5 ON/OFF ratio
Metal atoms storage
1,000 ON/OFF ratio
Stack complexity
Dedicated product line
10+ layers stack – many materials
Very thin layers (hard to manuf)
4 additional masks
Dedicated product line
10+ layers stack – many materials
Very thin layers (hard to manuf)
4 additional masks
Simple stack – Few materials
CapEx = new chamber on existing tool
Only 3 films and 2 masks
Process nodes 28/22 nm 28/22 nm 40 nm to 12 – 7 nm
Bit cell configuration
1T-1MTJ
20~40F2
200nm cell pitch limitation
2T-2MTJ
1T-1R
20~40F2
Read access time 25 ns 12.5 ns 15 ns
Write access time 200 ns 40 ns 10 us
Read energy 1pJ/bit - 0.2 pJ/bit
Write current 120 uA/bit - ~60 uA/bit
Standby current 200 uA 200 uA 2 uA
Data retention > 10 Yr - > 10 Yr
Endurance
> 1M
BER degrades with write cycles
> 100M > 1M
Operating temp
Up to 85 C
BER degrades with hot temp
Up to 85 C 125 C
Magnetic Immunity NO NO YES
COST

8. © 2019 CROSSBAR - www.crossbar-inc.com
Computing & Memory Trends in AI
8
CPU X86 CPU
AI training GPU, FPGA
Intel
Nvidia, Xilinx, Intel(Altera)
Graphcore, Kalray, Adapteva,
Quest
AI inference
(cloud)
Domain Specific ASICs
(e.g. TPU)
AI inference
(edge)
Domain Specific ASICs
(ARM, RISC-V based)
Google, Amazon, Microsoft,
WaveComputing, Bitmain,
Horizon-Robotics, Novumind
Intel(Movidius/Nervana),
Gyrfalcon, Habana, ThinCI,
WaveComputing Greenwave,
Syntiant, Mythic, Brainchip
CLOUDEDGE
Key players
SRAM + HBM +
SCM
SRAM + HBM
SRAM + ReRAM
eFlash, SRAM,
MRAM, ReRAM
Memory needs
ReRAM market (embedded)
ReRAM market (SCM)
ReRAM entering the AI market on AI inference edge platforms

9. © 2019 CROSSBAR - www.crossbar-inc.com 9
Events
Video
Images
Speech
Keywords
Sensors
Any data sources
Unstructured datasets
Camera, microphones,
sensors…
Neural Networks
Accelerators
Features/Vectors Extraction
<v1,v2,………..vn>
?
For some AI applications,
the classification phase
can take up to 3X the
time than the features
extraction with Neural
Network
Problem: Objects (vectors) Classification in AI
• There is a computing-intensive task required after every Neural Network

10. © 2019 CROSSBAR - www.crossbar-inc.com
Solution: ReRAM for Massive Search Hardware Acceleration
10
• Very wide Non-Volatile memory array
• 50 GB/s Read throughput (@8K wide)
• Applications
• Massive Search
• KNN
• 1000’s of Distance Calculators above
HPM
• Classifications
• CNN, RNN, NLP Inference
• Weights in ReRAM
• Embedded MACs
• Edge or Cloud
• Flexible architecture
• Number of instances, 8-bit to
binary.
• Scalable parallel processing with
chip to chip connection
• Spare memory enabling Learning at the
Edge
Simultaneous Processing
Deterministic Performance
Feature
Vector N
Legacy R/W Interface
……….
Read Bus 8192 bits
ReRAM Array
Highly Parallel
Read Interface
Feature
Vector 1
Feature
Vector N
Feature
Vector N-1
Feature
Vector 2
Computation
Engine
Computation
Engine
Computation
Engine
Computation
Engine

11. © 2019 CROSSBAR - www.crossbar-inc.com
3+ Billion Objects LookUp Per Second (OLUPS)
11
Object
length
OLUPS
1024 50,000,000
512 100,000,000
256 200,000,000
128 400,000,000
64 800,000,000
32 1,600,000,000
16 3,200,000,000
OLU/Watt
833,333,333
1,666,666,667
3,333,333,333
6,666,666,667
13,333,333,333
26,666,666,667
53,333,333,333
Scalable to 16 Billion
OLUPS per stick
-
500,000,000
1,000,000,000
1,500,000,000
2,000,000,000
2,500,000,000
3,000,000,000
3,500,000,000
1632641282565121024
OBJECTLOOKUPPERSECOND
OBJECT LENGTH
Billions of Object LookUp per Second
50MHz Crossbar XPU 1.5GHz ARM A53 + DDR4
30X improvement compared to ARM+DDR4
at 500X less power!
50MHz Crossbar XPU
1.5GHz ARM A53 + DDR4

12. © 2019 CROSSBAR - www.crossbar-inc.com
SCAiLE reference platforms
12
AI Innovators Join Forces in Consortium for Development and Commercialization
of Best-in-Class AI Computing Platform
Demos on Booth #311
Breaking News
NEW member !!

13. © 2019 CROSSBAR - www.crossbar-inc.com
Crossbar ReRAM:
Best Memory Technology Enabling AI
Based in Santa Clara, CA, U.S.A.
$100M+ in raised capital to date
Leader in Resistive RAM technology
New class of non volatile memory: Metal Filament Resistor
Back-end of line Non Volatile Memory: 40nm, 2xnm, 1xnm
Patented Technology: 310 filed / 160 issued
Applications in Storage Class Memory, AI, FPGAs, eNVM
Efficient search and computing with Highly Parallel Memory
13

14. © 2019 CROSSBAR - www.crossbar-inc.com
Resource Slide
14
More about Crossbar
www.crossbar-inc.com
More about SCAiLE
www.scaile.org
Embedded Vision Summit 2019
“Memory-centric hardware acceleration for
Machine Learning”
By Sylvain DUBOIS
VP Business Development & Marketing
May 22nd, 2019 – 1 pm
Come see our great demos !
Booth #311

15. © 2019 CROSSBAR - www.crossbar-inc.com 15
Thank you
Sylvain DUBOIS
sylvain.dubois@crossbar-inc.com
Linkedin: linkedin.com/in/link2sylvaindubois
Twitter: @syl20dubois