activity
20162026
most citedEnergy-Efficient Runtime Adaptable L1 STT-RAM Cache Design

29 citations · 51 across the 20 of their papers we have counts for

collaborators

22 papers

cs.AR2026

Surrogates, Spikes, and Sparsity: Performance Analysis and Characterization of SNN Hyperparameters on Hardware

Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija

Spiking Neural Networks (SNNs) offer inherent advantages for low-power inference through sparse, event-driven computation. However, the theoretical energy benefits of SNNs are ofte…

cs.AR2026

Device-Circuit Co-Design of Variation-Resilient Read and Write Drivers for Antiferromagnetic Tunnel Junction (AFMTJ) Memories

Yousuf Choudhary, Tosiron Adegbija

Antiferromagnetic Tunnel Junctions (AFMTJs) offer picosecond switching and high integration density for in-memory computing, but their ultrafast dynamics and low tunnel magnetoresi…

cs.AR2026

Accelerating Post-Quantum Cryptography via LLM-Driven Hardware-Software Co-Design

Yuchao Liao, Tosiron Adegbija, Roman Lysecky

Post-quantum cryptography (PQC) is crucial for securing data against emerging quantum threats. However, its algorithms are computationally complex and difficult to implement effici…

cs.AR2026

Antiferromagnetic Tunnel Junctions (AFMTJs) for In-Memory Computing: Modeling and Case Study

Yousuf Choudhary, Tosiron Adegbija

Antiferromagnetic Tunnel Junctions (AFMTJs) enable picosecond switching and femtojoule writes through ultrafast sublattice dynamics. We present the first end-to-end AFMTJ simulatio…

cs.NE2025

Spiking Neural Network Architecture Search: A Survey

Kama Svoboda, Tosiron Adegbija

This survey paper presents a comprehensive examination of Spiking Neural Network (SNN) architecture search (SNNaS) from a unique hardware/software co-design perspective. SNNs, insp…

cs.AR2024

Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture

Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija

Spiking Neural Networks (SNNs) offer potential advantages in energy efficiency but currently trail Artificial Neural Networks (ANNs) in versatility, largely due to challenges in ef…