7,348 research outputs found
Latency Optimized Asynchronous Early Output Ripple Carry Adder based on Delay-Insensitive Dual-Rail Data Encoding
Asynchronous circuits employing delay-insensitive codes for data
representation i.e. encoding and following a 4-phase return-to-zero protocol
for handshaking are generally robust. Depending upon whether a single
delay-insensitive code or multiple delay-insensitive code(s) are used for data
encoding, the encoding scheme is called homogeneous or heterogeneous
delay-insensitive data encoding. This article proposes a new latency optimized
early output asynchronous ripple carry adder (RCA) that utilizes single-bit
asynchronous full adders (SAFAs) and dual-bit asynchronous full adders (DAFAs)
which incorporate redundant logic and are based on the delay-insensitive
dual-rail code i.e. homogeneous data encoding, and follow a 4-phase
return-to-zero handshaking. Amongst various RCA, carry lookahead adder (CLA),
and carry select adder (CSLA) designs, which are based on homogeneous or
heterogeneous delay-insensitive data encodings which correspond to the
weak-indication or the early output timing model, the proposed early output
asynchronous RCA that incorporates SAFAs and DAFAs with redundant logic is
found to result in reduced latency for a dual-operand addition operation. In
particular, for a 32-bit asynchronous RCA, utilizing 15 stages of DAFAs and 2
stages of SAFAs leads to reduced latency. The theoretical worst-case latencies
of the different asynchronous adders were calculated by taking into account the
typical gate delays of a 32/28nm CMOS digital cell library, and a comparison is
made with their practical worst-case latencies estimated. The theoretical and
practical worst-case latencies show a close correlation....Comment: arXiv admin note: text overlap with arXiv:1704.0761
A guided tour of asynchronous cellular automata
Research on asynchronous cellular automata has received a great amount of
attention these last years and has turned to a thriving field. We survey the
recent research that has been carried out on this topic and present a wide
state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat
The Computational Cost of Asynchronous Neural Communication
Biological neural computation is inherently asynchronous due to large variations in neuronal spike timing and transmission delays. So-far, most theoretical work on neural networks assumes the synchronous setting where neurons fire simultaneously in discrete rounds. In this work we aim at understanding the barriers of asynchronous neural computation from an algorithmic perspective. We consider an extension of the widely studied model of synchronized spiking neurons [Maass, Neural Networks 97] to the asynchronous setting by taking into account edge and node delays.
- Edge Delays: We define an asynchronous model for spiking neurons in which the latency values (i.e., transmission delays) of non self-loop edges vary adversarially over time. This extends the recent work of [Hitron and Parter, ESA\u2719] in which the latency values are restricted to be fixed over time. Our first contribution is an impossibility result that implies that the assumption that self-loop edges have no delays (as assumed in Hitron and Parter) is indeed necessary. Interestingly, in real biological networks self-loop edges (a.k.a. autapse) are indeed free of delays, and the latter has been noted by neuroscientists to be crucial for network synchronization.
To capture the computational challenges in this setting, we first consider the implementation of a single NOT gate. This simple function already captures the fundamental difficulties in the asynchronous setting. Our key technical results are space and time upper and lower bounds for the NOT function, our time bounds are tight. In the spirit of the distributed synchronizers [Awerbuch and Peleg, FOCS\u2790] and following [Hitron and Parter, ESA\u2719], we then provide a general synchronizer machinery. Our construction is very modular and it is based on efficient circuit implementation of threshold gates. The complexity of our scheme is measured by the overhead in the number of neurons and the computation time, both are shown to be polynomial in the largest latency value, and the largest incoming degree ? of the original network.
- Node Delays: We introduce the study of asynchronous communication due to variations in the response rates of the neurons in the network. In real brain networks, the round duration varies between different neurons in the network. Our key result is a simulation methodology that allows one to transform the above mentioned synchronized solution under edge delays into a synchronized under node delays while incurring a small overhead w.r.t space and time
Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation
We investigate the performance of sparsely-connected networks of
integrate-and-fire neurons for ultra-short term information processing. We
exploit the fact that the population activity of networks with balanced
excitation and inhibition can switch from an oscillatory firing regime to a
state of asynchronous irregular firing or quiescence depending on the rate of
external background spikes.
We find that in terms of information buffering the network performs best for
a moderate, non-zero, amount of noise. Analogous to the phenomenon of
stochastic resonance the performance decreases for higher and lower noise
levels. The optimal amount of noise corresponds to the transition zone between
a quiescent state and a regime of stochastic dynamics. This provides a
potential explanation on the role of non-oscillatory population activity in a
simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9
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