Personal tools

Difference between revisions of "IBM Research"

From iis-projects

Jump to: navigation, search
(Available Projects)
(Available Projects)
Line 52: Line 52:
 
|-
 
|-
 
| MA || Developing Efficient Models of Strong AI for Intelligence Quotient (IQ) Test ||  [http://iis-projects.ee.ethz.ch/images/4/4b/IBM_RPM.pdf Developing Efficient Models of '''Strong AI for Intelligence Quotient (IQ) Test''']  || HDC || algorithmic design
 
| MA || Developing Efficient Models of Strong AI for Intelligence Quotient (IQ) Test ||  [http://iis-projects.ee.ethz.ch/images/4/4b/IBM_RPM.pdf Developing Efficient Models of '''Strong AI for Intelligence Quotient (IQ) Test''']  || HDC || algorithmic design
 +
|-
 +
| MA || Accurate deep learning inference using computational memory ||  [https://www.zurich.ibm.com/careers/2020_027.html Project description and application]  || IMC || algorithmic design
 
|-
 
|-
 
| SA || Testing of a computational memory chip || style="text-align: justify;"|TBA || IMC || PCB Design <br /> and/or<br />  µ-code implementation  
 
| SA || Testing of a computational memory chip || style="text-align: justify;"|TBA || IMC || PCB Design <br /> and/or<br />  µ-code implementation  

Revision as of 13:31, 12 November 2020

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

Hyperdimensional Computing (HDC)

NatureElectronics20.jpg

Hyperdimensional computing (HDC) is a brain-inspired computing paradigm based on representing information with hypervectors with dimensions in the thousands. Hypervectors are holographic and (pseudo)random with independent and identically distributed (i.i.d.) components. Principles of HDC allow implementing efficient machine learning models, where it has shown few-shot learning capabilities by requiring less training data than conventional machine learning models to do accurate predictions. By its very nature, HDC is extremely robust against failures, defects, variations, and noise, all of which are synonymous to ultra-low energy computation on nanoscale fabrics.

Useful Reading

In-Memory Computing (IMC)

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.

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. The work performed could span low-level hardware experiments on phase-change memory chips comprising more than 1 million devices to high-level algorithmic development in a deep learning framework such as TensorFlow or PyTorch. It also involves interactions with several researchers across IBM research focusing on various aspects of the project. The ideal candidate should have a multi-disciplinary background, strong mathematical aptitude and programming skills. Prior knowledge on emerging memory technologies such as phase-change memory is a bonus but not necessary.


Type Project Description Topic Workload Type
MA Lifelong learning challenge Artificial general intelligence (AGI): lifelong learning challenge HDC algorithmic design
MA Machine learning based on optimal transport using in-memory computing' Machine learning based on optimal transport using in-memory computing HDC algorithmic design
MA Developing Efficient Models of Strong AI for Intelligence Quotient (IQ) Test Developing Efficient Models of Strong AI for Intelligence Quotient (IQ) Test HDC algorithmic design
MA Accurate deep learning inference using computational memory Project description and application IMC algorithmic design
SA Testing of a computational memory chip TBA 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
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

Contact

Thesis will be at IBM Zurich in Rüschlikon
Hyperdimensional Computing (HDC) 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