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As the demand for efficient, low-power computing in embedded and edge devices grows, traditional computing methods are becoming less effective for handling complex tasks. Stochastic computing (SC) offers a promising alternative by…
Analog computing has reemerged as a promising avenue for accelerating deep neural networks (DNNs) due to its potential to overcome the energy efficiency and scalability challenges posed by traditional digital architectures. However,…
The increasing computational demands of modern AI systems have exposed fundamental limitations of digital hardware, driving interest in alternative paradigms for efficient large-scale inference. Dense Associative Memory (DenseAM) is a…
Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations…
This paper presents an in-memory computing (IMC) architecture for image denoising. The proposed SRAM based in-memory processing framework works in tandem with approximate computing on a binary image generated from neuromorphic vision…
Recent breakthroughs in associative memories suggest that silicon memories are coming closer to human memories, especially for memristive Content Addressable Memories (CAMs) which are capable to read and write in analog values. However, the…
This paper investigates hardware-based memory compression designs to increase the memory bandwidth. When lines are compressible, the hardware can store multiple lines in a single memory location, and retrieve all these lines in a single…
For the past four decades, cost and features have driven CMOS scaling. Severe lithography and material limitations seen below the 20 nm node, however, are challenging the fundamental premise of affordable CMOS scaling. Just continuing to…
Transformers, while revolutionary, face challenges due to their demanding computational cost and large data movement. To address this, we propose HyFlexPIM, a novel mixed-signal processing-in-memory (PIM) accelerator for inference that…
Computing-in-Memory (CIM) has shown great potential for enhancing efficiency and performance for deep neural networks (DNNs). However, the lack of flexibility in CIM leads to an unnecessary expenditure of computational resources on less…
Modern computing systems are capable of exascale calculations, which are revolutionizing the development and application of high-fidelity numerical models in computational science and engineering. While these systems continue to grow in…
There have been a plethora of research on multi-level memory devices, where the resistive random-access memory (RRAM) is a prominent example. Although it is easy to write an RRAM device into multiple (even quasi-continuous) states, it…
Analog Compute-In-Memory (CIM) architectures promise significant energy efficiency gains for neural network inference, but suffer from complex hardware-induced noise that poses major challenges for deployment. While noise-aware training…
Software managed byte-addressable hybrid memory systems consisting of DRAMs and NVMMs offer a lot of flexibility to design efficient large scale data processing applications. Operating systems (OS) play an important role in enabling the…
Binary matrix-vector multiplication (BMVM) is a key operation in post-quantum cryptography schemes like the Classic McEliece cryptosystem. Conventional computing architectures incur significant energy efficiency loss due to data movement of…
SRAM-based cache memory faces several scalability limitations in deep nanoscale technologies, e.g., high leakage current, low cell stability, and low density. Emerging Non-Volatile Memory (NVM) technologies have received lots of attention…
Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditional von-Neumann architecture. In this regard, interest in…
The emerging hybrid DRAM-NVM architecture is challenging the existing memory management mechanism in operating system. In this paper, we introduce memos, which can schedule memory resources over the entire memory hierarchy including cache,…
Analog processing-using-memory (PUM; a.k.a. in-memory computing) makes use of electrical interactions inside memory arrays to perform bulk matrix-vector multiplication (MVM) operations. However, many popular matrix-based kernels need to…
The ability to process signals in digital form depends on analog-to-digital converters (ADCs). Traditionally, ADCs are designed to ensure that the digital representation closely matches the analog signal. However, recent studies have shown…