“We can’t keep scaling compute, so the industry must scale efficiency instead.” – Kaoutar El Maghraoui – IBM Principal Research Scientist
“We can’t keep scaling compute, so the industry must scale efficiency instead.” – Kaoutar El Maghraoui, IBM Principal Research Scientist
This quote underscores a pivotal shift in AI development: as raw computational power reaches physical and economic limits, the focus must pivot to efficiency through optimized hardware, software co-design, and novel architectures like analog in-memory computing.1,2
Backstory and Context of Kaoutar El Maghraoui
Dr. Kaoutar El Maghraoui is a Principal Research Scientist at IBM’s T.J. Watson Research Center in Yorktown Heights, NY, where she leads the AI testbed at the IBM Research AI Hardware Center—a global hub advancing next-generation accelerators and systems for AI workloads.1,2 Her work centers on the intersection of systems research and artificial intelligence, including distributed systems, high-performance computing (HPC), and AI hardware-software co-design. She drives open-source development and cloud experiences for IBM’s digital and analog AI accelerators, emphasizing operationalization of AI in hybrid cloud environments.1,2
El Maghraoui’s career trajectory reflects deep expertise in scalable systems. She earned her PhD in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007, following a Master’s in Computer Networks (2001) and Bachelor’s in General Engineering from Al Akhawayn University, Morocco. Early roles included lecturing at Al Akhawayn and research on IBM’s AIX operating system—covering performance tuning, multi-core scheduling, Flash SSD storage, and OS diagnostics using IBM Watson cognitive tech.2,6 In 2017, she co-led IBM’s Global Technology Outlook, shaping the company’s AI leadership vision across labs and units.1,2
The quote emerges from her lectures and research on efficient AI deployment, such as “Powering the Future of Efficient AI through Approximate and Analog In-Memory Computing,” which addresses performance bottlenecks in deep neural networks (DNNs), and “Platform for Next-Generation Analog AI Hardware Acceleration,” highlighting Analog In-Memory Computing (AIMC) to reduce energy losses in DNN inference and training.1 It aligns with her 2026 co-authored paper “STARC: Selective Token Access with Remapping and Clustering for Efficient LLM Decoding on PIM Systems” (ASPLOS 2026), targeting efficiency in large language models via processing-in-memory (PIM).2 With over 2,045 citations on Google Scholar, her contributions span AI hardware optimization and performance.8
Beyond research, El Maghraoui is an ACM Distinguished Member and Speaker, Senior IEEE Member, and adjunct professor at Columbia University. She holds awards like the 2021 Best of IBM, IBM Eminence and Excellence for advancing women in tech, 2021 IEEE TCSVC Women in Service Computing, and 2022 IBM Technical Corporate Award. Leadership roles include global vice-chair of Arab Women in Computing (ArabWIC), co-chair of IBM Research Watson Women Network (2019-2021), and program/general co-chair for Grace Hopper Celebration (2015-2016).1,2
Leading Theorists in AI Efficiency and Compute Scaling Limits
The quote resonates with foundational theories on compute scaling limits and efficiency paradigms, pioneered by key figures challenging Moore’s Law extensions in AI hardware.
| Theorist | Key Contributions | Relevance to Quote |
|---|---|---|
| Cliff Young & Contributors (Google) | Co-authored “Scaling Laws for Neural Language Models” (2020, arXiv) and MLPerf benchmarks; advanced hardware-aware neural architecture search (NAS) for DNN optimization on edge devices.1 | Demonstrates efficiency gains via NAS, directly echoing El Maghraoui’s lectures on hardware-specific DNN design to bypass compute scaling.1 |
| Bill Dally (NVIDIA) | Pioneer of processing-in-memory (PIM) and tensor cores; authored works on energy-efficient architectures amid “end of Dennard scaling” (power density limits post-2000s).2 | Warns against endless compute scaling; promotes PIM and sparsity, aligning with El Maghraoui’s STARC paper and analog accelerators.2 |
| Jeff Dean (Google) | Formulated Chinchilla scaling laws (2022), showing optimal compute allocation balances parameters and data; co-developed TensorFlow and TPUs for efficiency.2 | Highlights diminishing returns of pure compute scaling, urging efficiency in training/inference—core to IBM’s AI Hardware Center focus.1,2 |
| Hadi Esmaeilzadeh (Georgia Tech) | Introduced neurocube and analog in-memory computing (AIMC) concepts (e.g., “Navigating the Energy Wall” papers); quantified AI’s “memory wall” and von Neumann bottlenecks.1 | Foundational for El Maghraoui’s AIMC advocacy, proving analog methods boost DNN efficiency by 10-100x over digital compute scaling.1 |
| Song Han (MIT) | Developed pruning, quantization, and NAS (e.g., TinyML, HAWQ frameworks); showed 90%+ parameter reduction without accuracy loss.1 | Enables “scale efficiency” for real-world deployment, as in El Maghraoui’s “Optimizing Deep Learning for Real-World Deployment” lecture.1 |
These theorists collectively established that post-Moore’s Law (transistor density doubling every ~2 years, slowing since 2010s), AI progress demands efficiency multipliers: sparsity, analog compute, co-design, and beyond-von Neumann architectures. El Maghraoui’s work operationalizes these at IBM scale, from cloud-native DL platforms to PIM for LLMs.1,2,6
References
1. https://speakers.acm.org/speakers/el_maghraoui_19271
2. https://research.ibm.com/people/kaoutar-el-maghraoui
3. https://github.com/kaoutar55
4. https://orcid.org/0000-0002-1967-8749
6. https://s3.us.cloud-object-storage.appdomain.cloud/res-files/1843-Kaoutar_ElMaghraoui_CV_Dec2022.pdf
7. https://www.womentech.net/speaker/all/all/69100
8. https://scholar.google.com/citations?user=yDp6rbcAAAAJ&hl=en
