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Mina Ibrahim

Publications and source records attributed to Mina Ibrahim.

3 recordsLinked to original sources

Retention-Aware RISC-V ISA Extension and Memory Controller on FPGA for MLC NVM

Non-volatile memory (NVM) technologies, particularly Multi-Level Cell (MLC) NVMs, offer significant potential for increasing memory density. MLC NVMs provide a tradeoff between write latency and retention time, where faster writes/stores result in lower retention and slower writes yield higher retention. However, limited work has been done to validate and prototype NVM-based systems in hardware, leveraging this tradeoff at the system level. In this paper, we present a novel memory controller architecture and a RISC-V instruction set extension to optimize MLC NVM write operations by balancing speed and retention time. Our custom NVM controller, built around a finite state machine with an AXI memory-mapped interface, efficiently manages read/write operations with enhanced burst transfers, minimizing latency. Additionally, we introduce a fast-store instruction in RISC-V to increasing write performance while addressing retention limitations. Further, we design a dedicated AXI slave peripheral that supports bit-significance-aware writes: critical bits (e.g., MSBs) are written using slower, high-retention writes, while non-critical bits (e.g., LSBs) use faster, low-retention writes to help enhance performance without compromising data reliability. These enhancements are implemented in hardware on an FPGA platform. Experimental results show that our controller reduces hardware overhead by 30% compared to conventional designs, and the fast-store instruction improves performance by over 7% for streaming workloads with less than 0.08% hardware overhead. The bit-wise AXI peripheral has a LUT utilization staying below 3.5% even for 64x64 matrices, and under 1% for 32x32 sizes, making it viable for integration into larger SoCs.

cs.AR

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders

Lengthy subject- or session-specific data acquisition and calibration remain a key barrier to deploying electroencephalography (EEG)-based brain-computer interfaces (BCIs) outside the laboratory. Previous work has shown that cross subject, cross-session invariant features exist in EEG. We propose a transfer learning pipeline based on a two-layer convolutional neural network (CNN) that leverages these invariants to reduce the burden of data acquisition and calibration. A baseline model is trained on EEG data from five able-bodied individuals and then rapidly updated with a small amount of data from a sixth, holdout subject. The remaining holdout data were used to test the performance of both the baseline and updated models. We repeated this procedure via a leave-one-subject out (LOSO) validation framework. Averaged over six LOSO folds, the updated model improved classification accuracy upon the baseline by 10.0, 18.8, and 22.1 percentage points on two binary and one ternary classification tasks, respectively. These results demonstrate that decoding accuracy can be substantially improved with minimal subject-specific data. They also indicate that a CNN-based decoder can be personalized rapidly, enabling near plug-and-play BCI functionality for neurorehabilitation and other time-critical EEG applications.

q-bio.QM

Early feasibility of an embedded bi-directional brain-computer interface for ambulation

Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research settings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80$\pm$0.08 between cues and decoded states over 5 runs.

q-bio.QM