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===Useful Reading===
 
===Useful Reading===
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*[https://arxiv.org/pdf/2010.01939.pdf Robust high-dimensional memory-augmented neural networks], arXiv, 2020
 
*[https://www.nature.com/articles/s41928-020-0410-3 In-memory hyperdimensional computing], Nature Electronics, 2020.
 
*[https://www.nature.com/articles/s41928-020-0410-3 In-memory hyperdimensional computing], Nature Electronics, 2020.
*[https://arxiv.org/pdf/2010.01939.pdf Robust high-dimensional memory-augmented neural networks], arXiv, 2020
 
 
*[https://www.nature.com/articles/s41928-020-00510-8 A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition], Nature Electronics, 2020.  
 
*[https://www.nature.com/articles/s41928-020-00510-8 A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition], Nature Electronics, 2020.  
 
*[https://link.springer.com/article/10.1007/s12559-009-9009-8 Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors], Cognitive Computation, 2009.
 
*[https://link.springer.com/article/10.1007/s12559-009-9009-8 Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors], Cognitive Computation, 2009.

Latest revision as of 20:53, 13 January 2021

IBM ZRLab.png

Short Description

Today, we are entering the era of cognitive computing, which holds great promise in deriving intelligence and knowledge from huge volumes of data. In today’s computers based on von Neumann architecture, huge amounts of data need to be shuttled back and forth at high speeds, a task at which this architecture is inefficient.

It is becoming increasingly clear that to build efficient cognitive computers, we need to transition to non-von Neumann architectures in which memory and processing coexist in some form. At IBM Research–Zurich in the Neuromorphic and In-memory Computing Group, we explore various such computing paradigms from in-memory computing to brain-inspired neuromorphic computing. Our research spans from devices and architectures to algorithms and applications.

About the IBM Research–Zurich

The location in Zurich is one of IBM’s 12 global research labs. IBM has maintained a research laboratory in Switzerland since 1956. As the first European branch of IBM Research, the mission of the Zurich Lab, in addition to pursuing cutting-edge research for tomorrow’s information technology, is to cultivate close relationships with academic and industrial partners, be one of the premier places to work for world-class researchers, to promote women in IT and science, and to help drive Europe’s innovation agenda. Download factsheet

Hybrid AI Systems (HAS)

NatureElectronics20.jpg

Neither symbolic AI nor neural networks alone has produced the kind of intelligence expressed in human and animal behavior. Why? Each has a long and rich history, but has addressed a relatively narrow aspect of the problem. Symbolic AI focuses on solving cognitive problems, drawing upon the rich framework of symbolic computation to manipulate internal representations in order to perform reasoning and inference. But it suffers from being non-adaptive, lacking the ability to learn from example or by direct observation of the world. Neural networks on the other hand have the ability to learn from data, and derive much of their power from nonlinear function approximation combined with stochastic gradient descent. But intelligence requires more than modeling input-output relationships. Without the richness of symbolic computation, neural nets lack the simple but powerful operations such as variable binding that allow for analogy making and reasoning, which underlie the ability to generalize from few examples.

We approach the problem from a very different perspective, inspired by the brain’s high-dimensional circuits and the unique mathematical properties of high-dimensional spaces. It leads us to a novel information processing architecture that combines the strengths of symbolic AI and neural networks, and yet has novel emergent properties of its own. By combining a small set of basic operations on high-dimensional vectors, we obtain hybrid AI system (HAS) that makes it possible to represent and manipulate data in ways familiar to us from symbolic AI, and to learn from the statistics of data in ways familiar to us from artificial neural networks and deep learning. Further, principles of such HAS allow few-shot learning capabilities, and extremely robust operations against failures, defects, variations, and noise, all of which are complementary to ultra-low energy computation on nanoscale fabrics such as phase-change memory devices. Exciting further research (listed in below table) awaiting in this direction spans high-level algorithmic exploration all the way to efficient hardware design for emerging computational fabrics.

Useful Reading

Prerequisites

  • Python
  • Background in machine learning (recommended)
  • Experience with any deep learning framework such as TensorFlow or PyTorch (recommended)
  • VLSI I (recommended)

In-Memory Computing (IMC)

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For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes increasingly data-centric and as the scalability limits in terms of performance and power are being reached, alternative computing paradigms are searched for in which computation and storage are collocated. A fascinating new approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. Computational Memory (CM) is finding application in a variety of areas such as machine learning and signal processing. In addition to novel non-volatile memory technologies like PCM and ReRAM, also conventional SRAM has been proposed as underlying storage technology in Computational Memories.

Useful Reading

Prerequisites

  • General interest in Deep Learning and memory/system design
  • VLSI I and VLSI II (recommended)
  • Analog Circuit Design (recommended)

Specific requirements for the different projects vary and are generally negotiable.

Available Projects

We are inviting applications from students to conduct their master’s thesis work or an internship project at the IBM Research lab in Zurich on this exciting new topic.

Type Project Description Topic Workload Type
MA Face Recognition at Scale Link to description HAS algorithmic design
MA Accelerating Transformers with Computational Memory Link to description HAS Hardware/algorithmic design
MA Developing Efficient Models of Strong AI for Intelligence Quotient (IQ) Test Link to description HAS algorithmic design
MA Lifelong learning challenge Link to description HAS algorithmic/hardware design
MA Machine learning based on optimal transport using in-memory computing' Link to description HAS hardware design
MA Accurate deep learning inference using computational memory Project description and application IMC algorithmic design
MA ADC design for computational memory Digital-to-Analog converters (DACs) and Analog-to-Digital converters (ADCs) are extensively employed in Computational Memory (CM) to handle the crossing between the digital and analog domain, in which computationally expensive tasks, like Matrix-Vector Multiplications (MVM), are carried out with O(1) complexity. Each conversion costs a certain amount of energy and its precision can only be guaranteed up to the Effective Number of Bits (ENOB) of the employed data converter.
The research focus will be on understanding the system level requirements on ADC and DAC for optimal performance of Deep Neural Network inference using CM. Furthermore, the effects of noise, non-linearity and manufacturing tolerances shall be examined and counter measurements, like for example periodic digital ADC recalibration and digital post processing, shall be evaluated with regards to effectivity and energy costs.
IMC analog circuit design
SA Testing of a computational memory chip This project is about building a Microprocessor/FPGA-based test platform around a novel IMC chip. After commissioning the chip, DL workload tests can be run to characterize its throughput and energy-efficiency. IMC PCB Design
and/or
µ-code implementation
MA/SA Neural Network Training on a
computational memory chip
TBA IMC algorithm/system design
and/or
analog circuit design

Contact

Thesis will be at IBM Zurich in Rüschlikon
Hybrid AI Systems (HAS) projects
Contact (at ETH Zurich): Dr. Frank K. Gurkaynak and Michael Hersche
Contact (at IBM): Dr. Abu Sebastian
Contact (at IBM): Dr. Abbas Rahimi
Professor: Luca Benini
In-Memory Computing (IMC) projects
Contact (at IBM/ETH Zurich): Riduan Khaddam-Aljameh
Contact (at IBM): Dr. Abu Sebastian
Professor: Luca Benini