My first brush with AI was a LISP class for my undergraduate degree. LISP, originated from MIT in 1958, was the language of choice for AI research and spawned a new class of computer hardware called LISP Machines in the 1980s. My first personal experience with AI was the HAL 9000 system from the 2001 Stanley Kubrik movie Space Odyssey. Today I have my own personal AI systems (Amazon echo and Apple Siri) that I rely on every day.
Most people don’t realize this but AI is already an active part of our daily lives: in our cars, in our phones, and in our homes. In fact, in regards to our cars, our lives will literally depend on AI, absolutely. I also believe the collective intelligence of the human race is on a downward trend so we will need all of the help we can get!
The challenge of AI of course is compute power which is good news for the semiconductor industry because that “need for speed” will consume leading edge silicon like there is no tomorrow. The fabless semiconductor ecosystem is already gearing up for this deep learning experience on embedded systems and this webinar is a quick example:
7 minutes could save you 7 months or more of hand porting and optimization
As Artificial Intelligence (AI) marches into almost every aspects of our lives, one of the major challenges is bringing this intelligence to small, low-power devices. This requires embedded platforms that can deliver extremely-high Neural Network performance with very low power consumption. However, that’s still not enough.
Machine Learning developers need a quick and automated way to convert and execute their pre-trained networks on such embedded platforms. In this session, we will discuss and demonstrate tools that complete this task within few minutes, instead of spending months on hand porting and optimizations.
Join CEVA experts to hear about:
- Overview of the leading deep learning frameworks, including Caffe and TensorFlow
- Various topologies of neural networks, including MIMO, FCN, MLPL
- Overview of most common neural networks such as Alexnet, VGG, GoogLeNet, ResNet, SegNet
- Challenges in porting neural networks to embedded platforms
- CEVA “Push button” conversion approach from pre-trained networks to real-time optimized
- Programmer Flow for CNN Acceleration
Computer vision engineers, Deep learning researchers, Project managers, marketing experts and others interested in embedded vision and machine learning.
Liran Bar, Director of Product Marketing, Imaging & Vision, CEVA
Erez Natan, Neural Network Team Leader, Imaging & Vision, CEVA
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About CEVA, Inc.
CEVA is the leading licensor of signal processing IP for a smarter, connected world. We partner with semiconductor companies and OEMs worldwide to create power-efficient, intelligent and connected devices for a range of end markets, including mobile, consumer, automotive, industrial and IoT. Our ultra-low-power IPs for vision, audio, communications and connectivity include comprehensive DSP-based platforms for LTE/LTE-A/5G baseband processing in handsets, infrastructure and machine-to-machine devices, computer vision and computational photography for any camera-enabled device, audio/voice/speech and ultra-low power always-on/sensing applications for multiple IoT markets. For connectivity, we offer the industry’s most widely adopted IPs for Bluetooth (Smart and Smart Ready), Wi-Fi (802.11 a/b/g/n/ac up to 4×4) and serial storage (SATA and SAS). Visit us at www.ceva-dsp.com and follow us on Twitter, YouTube and LinkedIn.