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EMC2: Energy Efficient Machine Learning and Cognitive Computing (5th edition)
Raj Parihar · Raj Parihar · Michael Goldfarb · Michael Goldfarb · Satyam Srivastava · TAO SHENG · Debajyoti Pal

Fri Dec 13 08:00 AM -- 06:40 PM (PST) @ West 306
Event URL: https://www.emc2-workshop.com/neurips-19 »

A new wave of intelligent computing, driven by recent advances in machine learning and cognitive algorithms coupled with process technology and new design methodologies, has the potential to usher unprecedented disruption in the way modern computing systems are designed and deployed. These new and innovative approaches often provide an attractive and efficient alternative not only in terms of performance but also power, energy, and area. This disruption is easily visible
across the whole spectrum of computing systems -- ranging from low end mobile devices to large scale data centers and servers including intelligent infrastructures.

A key class of these intelligent solutions is providing real-time, on-device cognition at the edge to enable many novel applications including computer vision and image processing, language understanding, speech and gesture recognition, malware detection and autonomous driving. Naturally, these applications have diverse requirements for performance, energy, reliability, accuracy, and security that demand a holistic approach to designing the hardware, software, and
intelligence algorithms to achieve the best power, performance, and area (PPA).

- Architectures for the edge: IoT, automotive, and mobile
- Approximation, quantization reduced precision computing
- Hardware/software techniques for sparsity
- Neural network architectures for resource constrained devices
- Neural network pruning, tuning and and automatic architecture search
- Novel memory architectures for machine learning
- Communication/computation scheduling for better performance and energy
- Load balancing and efficient task distribution techniques
- Exploring the interplay between precision, performance, power and energy
- Exploration of new and efficient applications for machine learning
- Characterization of machine learning benchmarks and workloads
- Performance profiling and synthesis of workloads
- Simulation and emulation techniques, frameworks and platforms for machine learning
- Power, performance and area (PPA) based comparison of neural networks
- Verification, validation and determinism in neural networks
- Efficient on-device learning techniques
- Security, safety and privacy challenges and building secure AI systems

Author Information

Raj Parihar (Microsoft)
Raj Parihar (Microsoft)
Michael Goldfarb (Nvidia)
Michael Goldfarb (Qualcomm)

SoC Architecture Research @ Qualcomm

Satyam Srivastava (Intel Corporation)
TAO SHENG (Amazon)
Debajyoti Pal (Cadence Design Systems, Inc.)