Vision intelligence has evolved over 300 million years, and accounts for 67% of brain activity, yet deep learning requires massive computational power that is typically centralized. Brodmann17 has developed a deep learning engine that runs visual recognition tasks on standard processors with better accuracy but significantly lower costs than traditional deep learning methods by using a non-redundant network architecture.

1. The rise of edge vision intelligence | Adi Pinhas

2. Vision is the key for autonomous
machines to reach their full potential

3. HOW HARD
CAN IT BE ?

4. VISION ACCOUNTS
FOR 67% OF THE
BRAIN ACTIVITY
in fact, half of all neural tissue
deals with vision in some way
Brodmann17

5. VISUAL SYSTEMS EXIST FOR 300,000,000 YEARS
Speech less than 200,000 years; writing less than 5,000 years

6. IMAGENET ERROR-RATE DROPS FAST
Breakthrough in some specific benchmarks

7. Mainframe
Centralized
1960-1970
Client-Server
Distributed
1980-2000
Edge Intelligence
Distributed
2020-
Mobile-Cloud
Centralized
2005-2020
The story of vision is the story of compute power
non mainstream
insufficient compute power
not too practical
network, privacy, latency…
high cost
heat, power, price

8. DEEP LEARNING
requires a massive amount
of computational power

9. Substantial processing power
Qualcomm 6-CoreARM high end 8-Core Nvidia TX1

10. BRODMANN17
DEVELOP DEEP
LEARNING ENGINE
for visual recognition tasks
on standard processors

11. Brodmann17’s deep learning has better accuracy
WITH SIGNIFICANTLY LOWER COSTS

15. Current approach
large redundant network
training
compress, squeeze, approximate
Our patented tech
no redundancy network
training

16. Enabling standard CPUs to see the world

17. THANK YOU!