15–19 Sept 2025
CERN
Europe/Zurich timezone

Accelerating AI with in-memory computing devices

15 Sept 2025, 16:10
5m
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium

CERN

400
Show room on map
7. Experimental Technologies Experimental Technologies

Speaker

Maurizio Pierini (CERN)

Description

Energy efficiency, while lowering the barrier to incorporating emerging device technologies into muture generations of computing systems must achieve higher processing speed and energy efficiency to support rapidly growing workloads under strict environmental constraints. To address this, domain-specific hardware accelerators have gained traction, with in-memory computing (IMC) emerging as a promising paradigm. By co-locating memory and computation, IMC reduces costly data movement and significantly improves energy efficiency. Digital IMC implementations provide precision and compatibility with existing design flows, while analog IMC offers the potential for greater energy savings and scalability by performing operations such as multiplication and accumulation directly in the analog domain. These complementary strengths motivate a unified design framework that can explore both approaches. In this context, hls4ml, a high-level synthesis tool originally developed for mapping machine learning algorithms onto FPGA and ASIC accelerators, provides a natural platform to investigate hybrid integration. Extending hls4ml to support digital and analog IMC models would enable systematic evaluation of trade-offs in accuracy, performance, and machine learning accelerators. Such integration would open pathways toward next-generation, domain-specialized hardware capable of meeting computational demands sustainably at future colliders

CERN group/ Experiment

EP-ESE, EP-CMG

Working area Area 7: Experimental Technologies
Project goals hls4ml support, technology assessment, prototype, (potential adoption of ComputeRAM IP?)
Timeline 3 years
Available person power 0
Additional person power request 1 doctoral student, 10% staff for supervision
Is this an already ongoing activity? No
Indicative hardware resources needs specific hardware (loan or purchase, as for the old techlab) + access to a GPU cluster with LCG-like software stack and cvmfs access with fast storage facilities across the full duration of the project

Authors

Andrea Rizzi (Universita & INFN Pisa (IT)) Davide Ceresa (CERN) Dr Emilio Meschi (CERN) Maurizio Pierini (CERN)

Presentation materials

There are no materials yet.