6 research outputs found
A Phase Change Memory and DRAM Based Framework For Energy-Efficient and High-Speed In-Memory Stochastic Computing
Convolutional Neural Networks (CNNs) have proven to be highly effective in various fields related to Artificial Intelligence (AI) and Machine Learning (ML). However, the significant computational and memory requirements of CNNs make their processing highly compute and memory-intensive. In particular, the multiply-accumulate (MAC) operation, which is a fundamental building block of CNNs, requires enormous arithmetic operations. As the input dataset size increases, the traditional processor-centric von-Neumann computing architecture becomes ill-suited for CNN-based applications. This results in exponentially higher latency and energy costs, making the processing of CNNs highly challenging.
To overcome these challenges, researchers have explored the Processing-In Memory (PIM) technique, which involves placing the processing unit inside or near the memory unit. This approach reduces data migration length and utilizes the internal memory bandwidth at the memory chip level. However, developing a reliable PIM-based system with minimal hardware modifications and design complexity remains a significant challenge.
The proposed solution in the report suggests utilizing different memory technologies, such as Dynamic RAM (DRAM) and phase change memory (PCM), with Stochastic arithmetic and minimal add-on logic. Stochastic computing is a technique that uses random numbers to perform arithmetic operations instead of traditional binary representation. This technique reduces hardware requirements for CNN\u27s arithmetic operations, making it possible to implement them with minimal add-on logic.
The report details the workflow for performing arithmetical operations used by CNNs, including MAC, activation, and floating-point functions. The proposed solution includes designs for scalable Stochastic Number Generator (SNG), DRAM CNN accelerator, non-volatile memory (NVM) class PCRAM-based CNN accelerator, and DRAM-based stochastic to binary conversion (StoB) for in-situ deep learning. These designs utilize stochastic computing to reduce the hardware requirements for CNN\u27s arithmetic operations and enable energy and time-efficient processing of CNNs.
The report also identifies future research directions for the proposed designs, including in-situ PCRAM-based SNG, ODIN (A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM), ATRIA (Bit-Parallel Stochastic Arithmetic Based Accelerator for In-DRAM CNN Processing), and AGNI (In-Situ, Iso-Latency Stochastic-to-Binary Number Conversion for In-DRAM Deep Learning), and presents initial findings for these ideas.
In summary, the proposed solution in the report offers a comprehensive approach to address the challenges of processing CNNs, and the proposed designs have the potential to improve the energy and time efficiency of CNNs significantly. Using Stochastic Computing and different memory technologies enables the development of reliable PIM-based systems with minimal hardware modifications and design complexity, providing a promising path for the future of CNN-based applications
Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
In this study, we present Q-Seg, a novel unsupervised image segmentation
method based on quantum annealing, tailored for existing quantum hardware. We
formulate the pixel-wise segmentation problem, which assimilates spectral and
spatial information of the image, as a graph-cut optimization task. Our method
efficiently leverages the interconnected qubit topology of the D-Wave Advantage
device, offering superior scalability over existing quantum approaches and
outperforming state-of-the-art classical methods. Our empirical evaluations on
synthetic datasets reveal that Q-Seg offers better runtime performance against
the classical optimizer Gurobi. Furthermore, we evaluate our method on
segmentation of Earth Observation images, an area of application where the
amount of labeled data is usually very limited. In this case, Q-Seg
demonstrates near-optimal results in flood mapping detection with respect to
classical supervised state-of-the-art machine learning methods. Also, Q-Seg
provides enhanced segmentation for forest coverage compared to existing
annotated masks. Thus, Q-Seg emerges as a viable alternative for real-world
applications using available quantum hardware, particularly in scenarios where
the lack of labeled data and computational runtime are critical.Comment: 12 pages, 9 figures, 1 tabl
AGNI: In-Situ, Iso-Latency Stochastic-to-Binary Number Conversion for In-DRAM Deep Learning
Recent years have seen a rapid increase in research activity in the field of
DRAM-based Processing-In-Memory (PIM) accelerators, where the analog computing
capability of DRAM is employed by minimally changing the inherent structure of
DRAM peripherals to accelerate various data-centric applications. Several
DRAM-based PIM accelerators for Convolutional Neural Networks (CNNs) have also
been reported. Among these, the accelerators leveraging in-DRAM stochastic
arithmetic have shown manifold improvements in processing latency and
throughput, due to the ability of stochastic arithmetic to convert
multiplications into simple bit-wise logical AND operations. However,the use of
in-DRAM stochastic arithmetic for CNN acceleration requires frequent stochastic
to binary number conversions. For that, prior works employ full adder-based or
serial counter based in-DRAM circuits. These circuits consume large area and
incur long latency. Their in-DRAM implementations also require heavy
modifications in DRAM peripherals, which significantly diminishes the benefits
of using stochastic arithmetic in these accelerators. To address these
shortcomings, this paper presents a new substrate for in-DRAM
stochastic-to-binary number conversion called AGNI. AGNI makes minor
modifications in DRAM peripherals using pass transistors, capacitors, encoders,
and charge pumps, and re-purposes the sense amplifiers as voltage comparators,
to enable in-situ binary conversion of input statistic operands of different
sizes with iso latency.Comment: (Preprint) To Appear at ISQED 202
BILP-Q: Quantum Coalition Structure Generation
Quantum AI is an emerging field that uses quantum computing to solve typical
complex problems in AI. In this work, we propose BILP-Q, the first-ever general
quantum approach for solving the Coalition Structure Generation problem (CSGP),
which is notably NP-hard. In particular, we reformulate the CSGP in terms of a
Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing
quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus,
we perform a comparative analysis in terms of time complexity between the
proposed quantum approach and the most popular classical baselines.
Furthermore, we consider standard benchmark distributions for coalition values
to test the BILP-Q on small-scale experiments using the IBM Qiskit environment.
Finally, since QUBO problems can be solved operating with quantum annealing, we
run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).Comment: 8 pages, 2 figures, 1 tabl