1,628 research outputs found
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
Cryogenic Memory Technologies
The surging interest in quantum computing, space electronics, and
superconducting circuits has led to new developments in cryogenic data storage
technology. Quantum computers promise to far extend our processing capabilities
and may allow solving currently intractable computational challenges. Even with
the advent of the quantum computing era, ultra-fast and energy-efficient
classical computing systems are still in high demand. One of the classical
platforms that can achieve this dream combination is superconducting single
flux quantum (SFQ) electronics. A major roadblock towards implementing scalable
quantum computers and practical SFQ circuits is the lack of suitable and
compatible cryogenic memory that can operate at 4 Kelvin (or lower)
temperature. Cryogenic memory is also critically important in space-based
applications. A multitude of device technologies have already been explored to
find suitable candidates for cryogenic data storage. Here, we review the
existing and emerging variants of cryogenic memory technologies. To ensure an
organized discussion, we categorize the family of cryogenic memory platforms
into three types: superconducting, non-superconducting, and hybrid. We
scrutinize the challenges associated with these technologies and discuss their
future prospects.Comment: 21 pages, 6 figures, 1 tabl
Product assurance technology for custom LSI/VLSI electronics
The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification
A Statistical STT-RAM Design View and Robust Designs at Scaled Technologies
Rapidly increased demands for memory in electronic industry and the significant technical scaling challenges of all conventional memory technologies motivated the researches on the next generation memory technology. As one promising candidate, spin-transfer torque random access memory (STT-RAM) features fast access time, high density, non-volatility, and good CMOS process compatibility. In recent years, many researches have been conducted to improve the storage density and enhance the scalability of STT-RAM, such as reducing the write current and switching time of magnetic tunneling junction (MTJ) devices. In parallel with these efforts, the continuous increasing of tunnel magneto-resistance(TMR) ratio of the MTJ inspires the development of multi-level cell (MLC) STT-RAM, which allows multiple data bits be stored in a single memory cell. Two types of MLC STT-RAM cells, namely, parallel MLC and series MLC, were also proposed. However, like all other nanoscale devices, the performance and reliability of STT-RAM cells are severely affected by process variations, intrinsic device operating uncertainties and environmental fluctuations. The storage margin of a MLC STT-RAM cell, i.e., the distinction between the lowest and highest resistance states, is partitioned into multiple segments for multi-level data representation. As a result, the performance and reliability of MLC STT-RAM cells become more sensitive to the MOS and MTJ device variations and the thermal-induced randomness of MTJ switching. In this work, we systematically analyze the impacts of CMOS and MTJ process variations, MTJ resistance switching randomness that induced by intrinsic thermal fluctuations, and working temperature changes on STT-RAM cell designs. The STT-RAM cell reliability issues in both read and write operations are first investigated. A combined circuit and magnetic simulation platform is then established to quantitatively study the persistent and non-persistent errors in STT-RAM cell operations. Then, we analyzed the extension of STT-RAM cell behaviors from SLC (single-level- cell) to MLC (multi-level- cell). On top of that, we also discuss the optimal device parameters of the MLC MTJ for the minimization of the operation error rate of the MLC STT-RAM cells from statistical design perspective. Our simulation results show that under the current available technology, series MLC STT-RAM demonstrates overwhelming benefits in the read and write reliability compared to parallel MLC STT-RAM and could potentially satisfy the requirement of commercial practices. Finally, with the detail analysis study of STT-RAM cells, we proposed several error reduction design, such as ADAMS structure, and FA-STT structure
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