arXiv subjects

Mohsen Riahi Alam

Publications and source records attributed to Mohsen Riahi Alam.

3 recordsLinked to original sources

arXiv2026

Audo-Sight: AI-driven Ambient Perception Across Edge-Cloud for Blind and Low Vision Users

Despite advances in assistive technologies, Blind and Low-Vision (BLV) individuals continue to face challenges in understanding their surroundings. Delivering concise, useful, and timely scene descriptions for ambient perception remains a long-standing problem in accessibility. Existing solutions often fail to identify user expectations for real-time and accessible responses. Moreover, for a given task, they either rely on cloud offloading, which imposes a significant delay, or edge-based AI, which often sacrifices accuracy. To address this, we present Audo-Sight, an AI-driven assistive system that spans across Edge-Cloud continuum and enables BLV individuals to perceive their surroundings through voice-based conversation. Audo-Sight provides low-latency, accurate, and human-friendly responses through a novel mechanism that seamlessly fuses Edge and Cloud responses. The system also addresses challenges in catering to BLV users through response editing informed by BLV needs. Audo-Sight orchestrates a set of AI models based on user query contextual analysis to infer intent and adjust for a variety of situations. In urgent cases where users require fast responses, Audo-Sight leverages parallel Edge and Cloud pipelines and seamlessly combines responses through its Response Fusion Engine. Systematic evaluation shows that Audo-Sight delivers speech output around 80% faster for urgent tasks and generates complete responses approximately 50% faster across all tasks compared to a commercial cloud-based solution---highlighting the need for customized AI-based solutions. Human evaluation of Audo-Sight shows that it is the preferred choice over GPT-5 for 62% of BLV participants with another 23% stating both perform comparably. Speed and interruption evaluations demonstrate that in most situations, the system can seamlessly respond at a rapid pace to keep up with BLV expectations.

cs.DC↗

arXiv2023

P2LSG: Powers-of-2 Low-Discrepancy Sequence Generator for Stochastic Computing

Stochastic Computing (SC) is an unconventional computing paradigm processing data in the form of random bit-streams. The accuracy and energy efficiency of SC systems highly depend on the stochastic number generator (SNG) unit that converts the data from conventional binary to stochastic bit-streams. Recent work has shown significant improvement in the efficiency of SC systems by employing low-discrepancy (LD) sequences such as Sobol and Halton sequences in the SNG unit. Still, the usage of many well-known random sequences for SC remains unexplored. This work studies some new random sequences for potential application in SC. Our design space exploration proposes a promising random number generator for accurate and energy-efficient SC. We propose P2LSG, a low-cost and energy-efficient Low-discrepancy Sequence Generator derived from Powers-of-2 VDC (Van der Corput) sequences. We evaluate the performance of our novel bit-stream generator for two SC image and video processing case studies: image scaling and scene merging. For the scene merging task, we propose a novel SC design for the first time. Our experimental results show higher accuracy and lower hardware cost and energy consumption compared to the state-of-the-art.

cs.ET↗

arXiv2022

Sorting in Memristive Memory

Sorting is needed in many application domains. The data is read from memory and sent to a general purpose processor or application specific hardware for sorting. The sorted data is then written back to the memory. Reading/writing data from/to memory and transferring data between memory and processing unit incur a large latency and energy overhead. In this work, we develop, to the best of our knowledge, the first architectures for in-memory sorting of data. We propose two architectures. The first architecture is applicable to the conventional format of representing data, weighted binary radix. The second architecture is proposed for the developing unary processing systems where data is encoded as uniform unary bitstreams. The two architectures have different advantages and disadvantages, making one or the other more suitable for a specific application. However, the common property of both is a significant reduction in the processing time compared to prior sorting designs. Our evaluations show on average 37x and 138x energy reduction for binary and unary designs, respectively, as compared to conventional CMOS off-memory sorting systems.

cs.ET↗