Related papers: WIRE: Write Energy Reduction via Encoding in Phase…

Emerging machine learning (ML) models (e.g., transformers) involve memory pin bandwidth-bound matrix-vector (MV) computation in inference. By avoiding pin crossings, processing in memory (PIM) can improve performance and energy for…

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer…

Binary embedding of high-dimensional data requires long codes to preserve the discriminative power of the input space. Traditional binary coding methods often suffer from very high computation and storage costs in such a scenario. To…

Modern DRAM modules are often equipped with hardware error correction capabilities, especially for DRAM deployed in large-scale data centers, as process technology scaling has increased the susceptibility of these devices to errors. To…

Efficient and coherent data retrieval and storage are essential for harnessing quantum algorithms' speedup. Such a fundamental task is addressed by a quantum Random Access Memory (qRAM). Despite their promising scaling properties, current…

As the performance gap between memory and processors has increased, then it leads to the poor performance. Efficient virtual memory can overcome this problem. And the efficiency of virtual memory depends on the replacement policy used for…

Spin-transfer-torque random access memory (STT-RAM) is a promising candidate for the next-generation of random-access-memory due to improved scalability, read-write speeds and endurance. However, the write pulse duration must be long enough…

Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices have been popularly used as synapses in crossbar array based analog Neural Network (NN) circuit to achieve more energy and time efficient data classification…

We report a new particle in cell (PIC) method based on the semi-implicit approach. The novelty of the new method is that unlike any of its semi-implicit predecessors at the same time retains the explicit computational cycle and conserves…

Computing has a huge memory problem. The memory system, consisting of multiple technologies at different levels, is responsible for most of the energy consumption, performance bottlenecks, robustness problems, monetary cost, and hardware…

Reducing the energy inefficiency of conventional CMOS-based computing devices -- which rely on logically irreversible gates to process information -- remains both a fundamental engineering challenge and a practical social challenge of…

With the popularity of wireless charging, energy access protection and cybersecurity are gaining importance, especially in public places. Currently, the most common energy encryption method uses frequency and associated impedance variation.…

Traditional memory writing operations proceed one bit at a time, where e.g. an individual magnetic domain is force-flipped by a localized external field. One way to increase material storage capacity would be to write several bits at a time…

Main memory's rising energy consumption has emerged as a critical challenge in modern computing architectures, particularly in large-scale systems, driven by frequent access patterns, growing data volumes, and insufficient power management…

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting features from the input signal and mapping them into higher dimensional spaces. Physical reservoir layers have been realized…

Spin Transfer Torque MRAMs are attractive due to their non-volatility, high density and zero leakage. However, STT-MRAMs suffer from poor reliability due to shared read and write paths. Additionally, conflicting requirements for data…

Recently, energy encryption for wireless power transfer has been developed for energy safety, which is important in public places to suppress unauthorized energy extraction. Most techniques vary the frequency so that unauthorized receivers…

In existing systems, the off-chip memory interface allows the memory controller to perform only read or write operations. Therefore, to perform any operation, the processor must first read the source data and then write the result back to…

Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memory wall and exploiting ultra-efficient analog low-precision arithmetic. Latest analog CIM…

Algorithms, data structures, coding techniques, and other methods that reduce bit-flips are being sought to best utilize hardware where flipping bits is the dominating cost. Write efficient memories were introduced by Ahlswede and Zhang as…