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Full-Text Articles in Digital Circuits
True-Bsg: A True Random Bit-Stream Generator For Fast And Efficient Stochastic Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
True-Bsg: A True Random Bit-Stream Generator For Fast And Efficient Stochastic Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
Faculty Scholarship
Stochastic computing (SC) leverages random bitstreams to perform arithmetic operations, offering ultra-lowcost, fault-tolerant, and highly parallelizable computations. The quality of these bit-streams is crucial for the accuracy and reliability of SC. This paper introduces TRUE-BSG, a novel true random bit-stream generator designed for fast and energyefficient SC. Unlike state-of-the-art (SoTA) pseudo-random and quasi-random bit-stream generators, TRUE-BSG utilizes a highquality true random number generator (TRNG), capable of producing random bits at a rate of 1 Gigabit per second. Our TRNG ensures high entropy and minimal correlation. TRUE-BSG shows comparable accuracy to software-based generators and better energy efficiency than SoTA bit-stream generators, …
Ams-Hd: Acute Mountain Sickness Detection With Hyperdimensional Computing, M. Hassan Najafi, Mehran Shoushtari Moghadam
Ams-Hd: Acute Mountain Sickness Detection With Hyperdimensional Computing, M. Hassan Najafi, Mehran Shoushtari Moghadam
Faculty Scholarship
Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient …
Breaking New Ground: Division Directly In Memory, M. Hassan Najafi, Mehran Shoushtari Moghadam
Breaking New Ground: Division Directly In Memory, M. Hassan Najafi, Mehran Shoushtari Moghadam
Faculty Scholarship
In-memory computing (IMC) has emerged as a promising paradigm for overcoming the limitations of traditional von Neumann architectures by reducing data movement and enhancing computational efficiency. Despite significant advancements in this area, implementing complex arithmetic operations, such as division, directly within memory has remained an elusive challenge. This paper introduces a pioneering technique for performing division operations directly in memory, representing the first successful integration of such functionality into the IMC framework. Our approach leverages an innovative circuit based on an unconventional model of computing–stochastic computing (SC). Our technique extends the computational capabilities of IMC systems and paves the way …
Trisc: Low-Cost Design Of Trigonometric Functions With Quasi Stochastic Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
Trisc: Low-Cost Design Of Trigonometric Functions With Quasi Stochastic Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
Faculty Scholarship
Low-cost and hardware-efficient design of trigonometric functions is challenging. Stochastic computing (SC), an emerging computing model processing random bit-streams, offers promising solutions for this problem. The existing implementations, however, often overlook the importance of the data converters necessary to generate the needed bit-streams. While recent advancements in SC bit-stream generators focus on basic arithmetic operations such as multiplication and addition, energy-efficient SC design of non-linear functions demands attention to both the computation circuit and the bit-stream generator. This work introduces TriSC, a novel approach for SC-based design of trigonometric functions enjoying state-of-the-art (SOTA) quasi-random bit-streams. Unlike SOTA SC designs of …
All You Need Is Unary: End-To-End Bit-Stream Processing In Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
All You Need Is Unary: End-To-End Bit-Stream Processing In Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
Faculty Scholarship
Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm introduced to achieve energy efficiency with a lightweight and single-pass training model. Hypervectors (HVs) at the heart of the HDC systems play a fundamental role in elevating the accuracy and obtaining the desired performance. Image-based HV encoding requires two types of HVs: Position and Level HVs. State-of-the-art approaches utilize pseudo-random methods for generating these HVs, which might degrade system performance and cause higher power consumption due to poor randomness in HV generation. These conventional methods require iteratively calculating orthogonal Positional HVs for acceptable accuracy. This work proposes a fast, ultra-lightweight, and high-quality …
Uhd: Unary Processing For Lightweight And Dynamic Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
Uhd: Unary Processing For Lightweight And Dynamic Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi
Faculty Scholarship
Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, …