540 research outputs found
A Review on Enhancement of SRAM Memory Cell
In this field research paper explores the design and analysis of Static Random Access Memory (SRAMs) that focuses on optimizing delay and power. CMOS SRAM cell consumes very little power and has less read and write time. Higher cell ratios will decrease the read and write time and improve stability. PMOS semiconductor unit with fewer dimensions reduces the ability consumption. During this paper, 6T SRAM cell is implemented with reduced power and performance is good according to read and write time, delay and power consumption. It's been noticed typically that increased memory capability will increase the bit-line parasitic capacitance that successively slows down voltage sensing, to avoid this drawback use optimized scaling techniques and more, get improve performance of the design. Memories are a core part of most of the electronic systems. Performance in terms of speed and power dissipation is the major area of concern in today's memory technology. During this paper SRAM cells supported 6T, 9T, and 8T configurations are compared based on performance for reading and write operations. During this paper completely different static random access memory is designed to satisfy low power, high-performance circuit and also the extensive survey on options of various static random access memory (SRAM) designs were reported. Improve performance static random access memory based on designing a low power SRAM cell structure with optimum write access power
Design of Low-Voltage Digital Building Blocks and ADCs for Energy-Efficient Systems
Increasing number of energy-limited applications continue to drive the demand for designing systems with high energy efficiency. This tutorial covers the main building blocks of a system implementation including digital logic, embedded memories, and analog-to-digital converters and describes the challenges and solutions to designing these blocks for low-voltage operation
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
Low-power digital processor for wireless sensor networks
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 69-72).In order to make sensor networks cost-effective and practical, the electronic components of a wireless sensor node need to run for months to years on the same battery. This thesis explores the design of a low-power digital processor for these sensor nodes, employing techniques such as hardwired algorithms, lowered supply voltages, clock gating and subsystem shutdown. Prototypes were built on both a FPGA and ASIC platform, in order to verify functionality and characterize power consumption. The resulting 0.18[micro]m silicon fabricated in National Semiconductor Corporation's process was operational for supply voltages ranging from 0.5V to 1.8V. At the lowest operating voltage of 0.5V and a frequency of 100KHz, the chip performs 8 full-accuracy FFT computations per second and draws 1.2nJ of total energy per cycle. Although this energy/cycle metric does not surpass existing low-energy processors demonstrated in literature or commercial products, several low-power techniques are suggested that could drastically improve the energy metrics of a future implementation.by Daniel Frederic Finchelstein.S.M
Reconfigurable nanoelectronics using graphene based spintronic logic gates
This paper presents a novel design concept for spintronic nanoelectronics
that emphasizes a seamless integration of spin-based memory and logic circuits.
The building blocks are magneto-logic gates based on a hybrid
graphene/ferromagnet material system. We use network search engines as a
technology demonstration vehicle and present a spin-based circuit design with
smaller area, faster speed, and lower energy consumption than the
state-of-the-art CMOS counterparts. This design can also be applied in
applications such as data compression, coding and image recognition. In the
proposed scheme, over 100 spin-based logic operations are carried out before
any need for a spin-charge conversion. Consequently, supporting CMOS
electronics requires little power consumption. The spintronic-CMOS integrated
system can be implemented on a single 3-D chip. These nonvolatile logic
circuits hold potential for a paradigm shift in computing applications.Comment: 14 pages (single column), 6 figure
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
Temperature Variation Operation of Mixed-VT 3T GC-eDRAM for Low Power Applications in 2Kbit Memory Array
Embedded memories were once utilized to transfer information between the CPU and the main memory. The cache storage in most traditional computers was static-random-access-memory (SRAM). Other memory technologies, such as embedded dynamic random-access memory (eDRAM) and spin-transfer-torque random-access memory (STT-RAM), have also been used to store cache data. The SRAM, on the other hand, has a low density and severe leakage issues, and the STT-RAM has high latency and energy consumption when writing. The gain-cell eDRAM (GC-eDRAM), which has a higher density, lower leakage, logic compatibility, and is appropriate for two-port operations, is an attractive option. To speed up data retrieval from the main memory, future processors will require larger and faster-embedded memories. Area overhead, power overhead, and speed performance are all issues with the existing architecture. A unique mixed-V_T 3T GC-eDRAM architecture is suggested in this paper to improve data retention times (DRT) and performance for better energy efficiency in embedded memories. The GC-eDRAM is simulated using a standard complementary-metal-oxide-semiconductor (CMOS) with a 130nm technology node transistor. The performance of a 2kbit mixed-V_T 3T GC-eDRAM array were evaluated through corner process simulations. Each memory block is designed and simulated using Mentor Graphics Software. The array, which is based on the suggested bit-cell, has been successfully operated at 400Mhz under a 1V supply and takes up almost 60-75% less space than 6T SRAM using the same technology. When compared to the existing 6T and 4T ULP SRAMs (others' work), the retention power of the proposed GC-eDRAM is around 80-90% lower
Application-Specific SRAM Design Using Output Prediction to Reduce Bit-Line Switching Activity and Statistically Gated Sense Amplifiers for Up to 1.9x Lower Energy/Access
This paper presents an application-specific SRAM design targeted towards applications with highly correlated data (e.g., video and imaging applications). A prediction-based reduced bit-line switching activity scheme is proposed to reduce switching activity on the bit-lines based on the proposed bit-cell and array structure. A statistically gated sense-amplifier approach is used to exploit signal statistics on the bit-lines to reduce energy consumption of the sensing network. These techniques provide up to 1.9 Ă— lower energy/access when compared with an 8T SRAM. These savings are in addition to the savings that are achieved through voltage scaling and demonstrate the advantages of an application-specific SRAM design.Texas Instruments Incorporate
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