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117 records found

Jumping Shift

A Logarithmic Quantization Method for Low-Power CNN Acceleration

Logarithmic quantization for Convolutional Neural Networks (CNN): a) fits well typical weights and activation distributions, and b) allows the replacement of the multiplication operation by a shift operation that can be implemented with fewer hardware resources. We propose a new ...
DSP blocks are one of the efficient solutions to implement multiply-accumulate (MAC) operations on FPGAs. However, since the DSP blocks have wide multiplier and adder blocks, MAC operations using low bit-length parameters lead to an underutilization. Hence, an efficient approxima ...
In multi-sensor systems, several sensors produce data streams, commonly, at different frequencies. If they are let running wild without synchronization, after a period of time, they are likely to be disordered, presenting as simultaneous measures that have been recorded at differ ...
Quantization techniques are widely used in CNN inference to reduce the cost of hardware at the expense of small accuracy losses. However, after the quantization, there is still a multiplication cost for the fixed-point quantized CNN weights. Therefore, a novel CNN quantization te ...
Computation capability characteristics of neuromorphic analog/mixed-signal spiking neural networks offer capable platform for implementation of cognitive tasks on resource-limited embedded platforms. In this paper, we derive stochastic model of spiking neural processing systems f ...
Advanced driving assistance systems (ADAS) prepave regulators, consumers and corporations for the medium-term reality of autonomous driving with adaptive cruise control, collision avoidance and lane departure warning system. Various sensors like camera, RADAR and LIDAR, integrate ...
Energy-efficiency and computation capability characteristics of analog/mixed-signal spiking neural networks offer capable platform for implementation of cognitive tasks on resource-limited embedded platforms. However, inherent mismatch in analog devices severely influence accurac ...
In pulse-based neural networks, synaptic dynamics can have direct influence on learning of neural codes, and encoding of spatiotemporal spike patterns. In this paper, we propose an adaptive synapse circuit for increased flexibility and efficacy of signal processing units in neuro ...
Synaptic dynamics is of great importance in realizing biophysically accurate neural behaviors and efficient synaptic learning in neuromorphic integrated circuits. In this paper, we propose a current-based synapse structure with multi-compartment receptors AMPA, NMDA and GABAa and ...
Simulating large spiking neural networks with a high level of realism in a FPGA requires efficient network architectures that satisfy both the resource and interconnect constraints, as well as the changes in traffic patterns due to learning processes. In this paper, we propose a ...
The pathophysiological processes underlying the ECG tracing demonstrate significant heart rate and the morphological pattern variations, for different or in the same patient at diverse physical/temporal conditions. Within this framework, spiking neural networks (SNN) may be a com ...
Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron si ...
The high level of realism of spiking neuron networks and their complexity require a substantial computational resources limiting the size of the realized networks. Consequently, the main challenge in building complex and biologically-accurate spiking neuron network is largely set ...
In this paper, we present the Immediate Neighbourhood Temperature (INT) routing algorithm which balances thermal profiles across dynamically-throttled 3D NoCs by adaptively routing interconnect traffic based on runtime temperature monitoring. INT avoids the overheads of system-wi ...

Fighting Dark Silicon

Toward Realizing Efficient Thermal-Aware 3-D Stacked Multiprocessors

This paper investigates the challenges of dark silicon that impede the performance and reliability of 3-D stacked multiprocessors. It presents a multipronged approach toward addressing the thermal issues arising from high-density integration in die stacks, spanning architectural ...
This paper presents a wideband linear direct digital RF modulator (DDRM) in 40nm CMOS technology. It features an advanced 2nd-order-hold interpolation filter and I/Q-interleaving harmonic rejection RF DACs. The 2×9-bit DDRM core occupies 0.21mm2 and consumes only 110mW at 1 GHz. ...
To fully benefit from the progress of CMOS technologies, it is desirable to completely digitize the TX, replacing its final stage with a digitally controlled PA (DPA). The DPA consists of arrays of small sub-PAs that are digitally controlled to modulate the output amplitude, thus ...
This paper presents an advanced 2.3-2.8 GHz fully-integrated digital-intensive polar Doherty transmitter realized in 40nm standard CMOS. The proposed architecture comprises CORDIC, digital delay aligners, interpolators, digital pre-distortion (DPD) circuitry in combination wi ...
In a neuromorphic integrated circuit synaptic dynamics are of great importance to capture accurate neural behaviors. In this paper, we propose a current-based synapse design mediated with multiple receptor types, namely AMPA, NMDA and GABAa, and a weight-dependent learning algori ...
In this paper, we propose a reconfigurable neural spike classifier based on neuromorphic event-based networks that can be directly interfaced to neural signal conditioning and quantization circuits. The classifier is set as a heterogeneity based, multi-layer computational network ...