Alex Pappachen James []

Neuromorphic Computing - Circuits and Systems; Pattern recognition and machine learning in hardware

"Artificial Intelligence - Be afraid of the evolution of stupidity exhibited by some natural brains; not the intelligent silicon brain"

Dr. Alex James works in the area of memristive neuromorphic systems, VLSI and image processing. He completed his Ph.D. in a short 2-year duration from Queensland Micro-and Nanotechnology center, Griffith University. He is currently chairing the Electrical and Computer Engineering Department at Nazarbayev University. He has been a faculty member in Nazarbayev University since 2013. He is the founding chair of IEEE Kazakhstan subsection, mentor to IEEE Student branch and CASS Chapter at Nazarbayev University and a member of IEEE CASS technical committee on Nonlinear Circuits and Systems. He was the founding chair of IEEE CASS Kerala chapter and was a member of IET Vision and Imaging Network. He was an editorial board member of Information Fusion (2010-2014), and currently serving associate editor of IEEE Access, HCIS and IEEE Transactions on Circuits and System 1 journal. He has several years of experience in managing industry consulting projects and academic projects in board design and pattern recognition circuits, and data and business analytics consulting for IT and semiconductor industry. He also received a certificate of appreciation and award from the President of the Republic of Kazakhstan for excellence in education in 2017. He is a Senior Member of IEEE, Life Member of ACM, and Senior Fellow of HEA.

Latest news!

A hybrid memristor–CMOS chip for AI, Nature Electronics, July 15, 2019 

A book for school children about the life of scientist Dr EK Janaki Ammal published by Vidyarthi Vigyan Manthan and Vigyan Bharati.  [English] [comming soon - Hindi, Marathi, Tamil, Telugu, Bengali]

Latest news!

26th IEEE International Conference on Electronics Circuits and Systems (ICECS 2019) , Special Session: Reliability, robustness and signal integrity challenges for Processing-In-Emerging-Memory (PIEM) systems and neural architectures; Chairs: Alex James (Nazarbayev University), Bhaskar Choubay (Fraunhofer/Seigen), Jai Narayan Tripathi (STMicroelectronics), Helen Li (Duke University), Georgios Ch. Sirakoulis (DUTH)  

Latest News! -  Deep Learning Classifiers with Memristive Networks 

Deep Learning Classifiers with Memristive Networks 

This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.


  • Offers an introduction to deep neural network architectures
  • Describes in detail different kind of neuro-memristive systems, circuits and models
  • Shows how to implement different kind of neural networks in analog memristive circuits


Latest news! - 6 papers in ISCAS 2019

  1. A. Dorzhigulov, A. James, Generalized Bell-Shaped Membership Function Generation Circuit for Memristive Neural Networks, ISCAS 2019
  2. K. Aliakhmet, A. James, Temporal G-Neighbor Filtering for Analog Domain Noise Reduction in Astronomical Videos, ISCAS 2019 [TCAS-2]
  3. Y. Akhmetov, A. James, Probabilistic Neural Network with Memristive Crossbar Circuits, ISCAS 2019
  4. O. Krestinskaya, A. Irmanova, A. James, Memristive Non-Idealities: Is There Any Practical Implications for Designing Neural Network Chips?, ISCAS 2019
  5. [TCAS-1] O. Krestinskaya, K. Salama, A. James, Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits, ISCAS 2019
  6. Krestinskaya O, Choubey B., James AP, AM-DCGAN: Analog Memristive Hardware Accelerator for Deep Convolutional Generative Adversarial Networks, 2019 ISCAS LBN accepted, 2019 [TCAS2]

Latest news!

IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2019), Special Session: Emerging Memory Technologies for Neuromorphic Circuits and Systems; Chairs: Jason Eshraghian (University of Western Australia), Alex James (Nazarbayev University), Herbert Ho-Ching Iu (University of Western Australia)

EHETL Talk - Infinite professors - Higher education leaders forum - in a rat race

 Latest news! - 3 papers in ISCAS 2018

  1. [Conference] Olga Krestinskaya, Khaled Salama, Alex James, Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks, IEEE International Symposium on Circuits and Systems 2018 (ISCAS 2018)
  2. [Conference] Darya Mikhailenko, Alex James, Kaushik Roy, M^2CA: Modular Memristive Crossbar Arrays, IEEE International Symposium on Circuits and Systems 2018 (ISCAS 2018)
  3. [Conference] A. P. James, I. Fedorova, T. Ibrayev and D. Kudithipudi, HTM Spatial Pooler with Memristor Crossbar Circuits for Sparse Biometric Recognition, IEEE International Symposium on Circuits and Systems 2018 (ISCAS 2018)

New Book!

Memristor and Memristive Neural Networks

Memristor and Memristive Neural Networks
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor. The modelling of the memristors is required to ensure that the devices can be put to use and improve emerging application. In this book, various memristor models are discussed, from a mathematical framework to implementations in SPICE and verilog, that will be useful for the practitioners and researchers to get a grounding on the topic. The applications of the memristor models in various neuromorphic networks are discussed covering various neural network models, implementations in A/D converter and hierarchical temporal memories.