Hsinyu Tsai (Sidney)

Department

Analog AI and Design Automation group at Almaden Research Lab

Job Title

Manager, Principal Research Staff Member

INSTRUCTION LANGUAGES

Mandarin, English

PERSONAL BIO

HsinYu (Sidney) Tsai received her Ph.D. from the Electrical Engineering and Computer Science department at Massachusetts Institute of Technology in 2011. After graduation, Sidney joined the Nanofabrication and Electron Beam Lithography group at the IBM T.J. Watson Research Center as Research Staff Member and developed directed self-assembly (DSA) lithography for fabricating finFETs. From 2015-2016, Sidney managed the Advanced Lithography group in the Microelectronics Research Laboratory (MRL), managing operations of a 200mm research prototyping line.

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Sidney currently works in the Alamden Research Center in San Jose, CA, as a Principal Research Staff Member and manager of the Analog AI group. Analog AI based on Phase Change Memory (PCM) devices utilizes emerging non-volatile memory embedded in the backend to compute vector-matrix multiplication at the location of data, achieving high power performance for Deep Learning workloads in the Cloud and on the edge. The group demonstrates software compatible accuracies for both training and inference of Deep Neural Networks (DNNs), highlighted in two Nature publications in 2018 and 2023. The group is now focusing on developing large scale, configurable hardware for inference and AI for design automation.

WORK EXPERIENCE AND AWARDS

Manager, Analog AI group at IBM Almaden Research Lab, USA (2020-present)
Team member, AI Hardware Accelerator groups at IBM Almaden Research Lab, USA (2016-2020)
Manager, Advanced Lithography Group at IBM Thomas J Watson Lab, USA (2015-2016)
Team member, E-beam Lithography Group at IBM Thomas J Watson Lab, USA (2011-2015)

EDUCATIONAL BACKGROUND

PhD in Electrical Engineering & Computer Science Department, Massachusetts Institute of Technology, USA
Master in Electrical Engineering & Computer Science Department, Massachusetts Institute of Technology, USA
Bachelor in Electrical Engineering and Physics, National Taiwan University, Taiwan

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VacancIES

Brief Introduction of the Department

The IBM Analog AI team is global team focusing on advancing the forefront of analog in-memory computing (AIMC) technologies to overcome the Von-Neumann Bottleneck. By performing vector-matrix multiplication (VMM) at the location where data is stored, we save time and energy by reducing data communication between memory and the compute unit. This efficient VMM computation is particularly powerful in deep neural network (DNN) applications, where the VMMs can tolerate a certain level of errors, for example, due to reduced-precision or noisy computations, without effecting accuracy of the DNN model. The team demonstrated near software-equivalent DNN accuracies both in phase change memory (PCM) hardware and in software simulations. We published high profile papers in Nature, VLSI, and IEDM.

The Silicon Valley Lab Research team holds technical leadership roles in a broad range of topics, including AIMC hardware testing, circuit design, algorithm simulations, application exploration, architecture definition, and software development. The team has hosted many intern students and visiting scholars in the past from around the world, including US, Europe, Brazil, Japan, and Taiwan. Some students are now regular employees at IBM and many continued to advance their careers in related fields.

Department Staff Number

5-10 people

Internship Job Description

As part of the IBM Research Semiconductor team, you will conduct world-class research on AI Hardware architecture, algorithm or design for Deep Neural Network acceleration. In this internship, the student will develop software stack and/or hardware components for a memory-centric computing architecture. Research could include Deep Neural Network accuracy simulations, hardware demonstrations, and power performance modeling. The student will gain hands-on experience implementing DNN workloads, such as CNN and Transformers, for deployment onto hardware chip and in simulations.

Preferred Intern Education Level

Undergraduate students (third year and above)
Master students
Doctoral students

Preferred Intern Skill Sets or Qualities

Familiar with Python, PyTorch, C++
Knowledge on Transformers or in-memory computing architecture or Analog circuit design is a plus

Internship Period

From June 14 to September 5, 2027 (adjustable at the discretion of the organisation)

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Others

Expected working hours per week: 40 hours
This internship position will be in San José.
Salary/Financial Support: Voyage of Aspiration Intern Scholarship
Benefits will not be provided.
The DS-2019 fee will be covered by IBM Research or NTU. Students are responsible for the VISA application fee.

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