Call for Papers:

CogArch Workshop at HPCA 2022

Abstract or Paper Registration Deadline
December 24, 2021
Final Submission Deadline
February 4, 2022

CogArch 2022: 6th Workshop on Cognitive Architectures (Co-located with HPCA)

Artificial Intelligence (AI) and Machine Learning (ML) techniques have become the de facto solution to drive human progress and more specifically, automation. In the last years, the world’s economy has been gravitating towards the AI/ML domain (from industrial and scientific perspectives) and the expectation of growth is not withering away. Additionally, these trends have been further exacerbated by the ongoing global COVID-19 pandemic, which paralyzed the world’s economy and made evident the need for even more automation to provide safer and more reliable services and also to aid in the agile discovery of life-saving drugs and vaccines —all this with appropriate security and data privacy elements in place. To support those kinds of applications, “cognitive” (AI/ML) architectures are designed and deployed to materialize advances in the aforementioned fields. However, although the newest cognitive designs are improving by the day, the number of challenges ahead for these systems is still overwhelming. With existing solutions reaching functional maturity, design considerations are now pivoting to new aspects like energy scaling, reliable operation, safe guarantees or even security and data-privacy properties. Specifically, when it comes to security and data privacy in the AI/ML context, Homomorphic Encryption (HE) has emerged as a highly promising approach. HE is arguably the holy grail of data-secure computing as it provides security and privacy guarantees by allowing computation on encrypted private data without the need for decryption. This is particularly enticing for applications in the medical sciences, natural language processing, autonomous and connected vehicles, as well as traditional domains such as banking systems, where HE could drastically reduce the frequency of data breaches, thus guaranteeing privacy of highly sensitive user data.

In this context, this edition of the CogArch workshop aims at bringing together the necessary know-how to design cognitive architectures from a holistic point of view, tackling all their design considerations from the algorithms to platforms in all the different fields that cognitive architectures will soon occupy, from autonomous cars to critical tasks in avionics, finance, space travel or even personalized medicine. This year’s edition, in addition, solicits contributions on the security and data-privacy preserving aspects of AI/ML and related application domains.

The CogArch workshop already had five successful editions, bringing together experts and knowledge on the most novel design ideas for cognitive systems. This workshop capitalizes on the synergy between industrial and academic efforts in order to provide a better understanding of cognitive systems and key concepts of their design.

Call for Papers

Hardware and software design considerations are gravitating towards AI applications, as those have been proven extremely useful in a wide variety of fields, from edge computing in autonomous cars, to cloud-based computing for personalized medicine. The recent years have brought about a boom in start-ups and novel platforms that constantly offer improvements in performance and accuracy for the aforementioned applications. As this kind of cognitive architectures evolve, system designers must incorporate many different considerations, with security and data privacy being the key ones today. The emergence of different security and data privacy approaches for AI/ML applications, including but not limited to the use of Homomorphic Encryption (HE) techniques, is also leading to a very diverse set of (hardware and software) design decisions and solutions.

The CogArch workshop solicits formative ideas and new product offerings in this general space that covers all the design aspects of cognitive systems, with particular focus this year on the security and data privacy considerations of AI/ML.

Topics of interest include (but are not limited to):
– Hardware support for state-of-the-art (post-quantum) encryption techniques
– Hardware-software co-design and acceleration of homomorphic encryption techniques
– Demonstration of side-channel or adversarial attacks on AI systems and/or potential solutions, including hardware support for mitigation of these attacks
– Prototype demonstrations of state-of-the-art secure AI systems
– System-level techniques to accelerate end-to-end execution (inference and/or training) of secure AI computation
– Secure algorithms in support of cognitive reasoning: recognition, intelligent search, diagnosis, inference and informed decision-making.
– Swarm intelligence and distributed architectural support; brain-inspired and neural computing architectures.
– Prototype demonstrations of state-of-the-art cognitive computing systems.
– Accelerators and micro-architectural support for artificial intelligence.
– Cloud-backed autonomics and mobile cognition: architectural and OS support thereof.
– Resilient design of distributed (swarm) mobile AI architectures.
– Reliability and safety considerations, and security against adversarial attacks in mobile AI architectures.
– Techniques for improving energy efficiency, battery life extension and endurance in mobile AI architectures.
– Case studies and real-life demonstrations/prototypes in specific application domains: e.g. smart homes, connected cars and UAV-driven commercial services, architectures in support of AI for healthcare applications, such as medical imaging, drug discovery and smart diagnostics, as well as applications of interest to defense and homeland security.

The workshop shall consist of regular presentations and/or prototype demonstrations by authors of selected submissions. In addition, it will include invited keynotes by eminent researchers from industry and academia as well as interactive panel discussions to kindle further interest in these research topics. Submissions will be reviewed by a workshop Program Committee, in addition to the organizers.

Submitted manuscripts must be in English of up to 2 pages (with same formatting guidelines as the main conference) indicating the type of submission: regular presentation or prototype demonstration. Submissions should be submitted to the following link by December 24th, 2021 ( If you have questions regarding submission, please contact us:

Call For Prototype Demonstrations

CogArch will feature a session where researchers can showcase innovative prototype demonstrations or proof-of-concept designs in the cognitive architecture space. Examples of such demonstrations may include (but are not limited to):

– Custom ASIC or FPGA-based demonstrations of machine learning, cognitive or neuromorphic architectures.
– Innovative implementations of state-of-the-art cognitive algorithms/applications, and the underlying software-hardware co-design techniques.
– Demonstration of end-to-end cognitive systems comprising of edge devices backed by a cloud computing infrastructure.
– Novel designs showcasing the adoption of emerging technologies for the design of cognitive systems.
– Tools or frameworks to aid analysis, simulation and design of cognitive systems.

Submissions for the demonstration session may be made in the form of a 2-page manuscript highlighting key features and innovations of the prototype demonstration. Proposals accepted for demonstration during the workshop can be accompanied by a poster/short presentation. Authors should explicitly indicate that the submission is for prototype demonstration at submission time.

Important Dates

Paper submission deadline: December 24th, 2021
Notification of acceptance: January 21st, 2022
Workshop date: February 12th or 13th, 2022 (TBD)


Roberto Gioiosa, Pacific Northwest National Laboratory
David Trilla, IBM Research
Subhankar Pal, IBM Research
Saransh Gupta, IBM Research
Augusto Vega, IBM Research
Karthik Swaminathan, IBM Research
Alper Buyuktosunoglu, IBM Research
Pradip Bose, IBM Research
Nir Drucker, IBM Research