Minimizing Test Power in SRAM through Reduction of Pre-charge Activity

In this paper we analyze the test power of SRAM memories and demonstrate that the full functional pre-charge activity is not necessary during test mode because of the predictable addressing sequence. We exploit this observation in order to minimize power dissipation during test by eliminating the unnecessary power consumption associated with the pre-charge activity. This is achieved through a modified pre-charge control circuitry, exploiting the first degree of freedom of March tests, which allows choosing a specific addressing sequence. The efficiency of the proposed solution is validated through extensive Spice simulations

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

An event-based architecture for solving constraint satisfaction problems

Constraint satisfaction problems (CSPs) are typically solved using conventional von Neumann computing architectures. However, these architectures do not reflect the distributed nature of many of these problems and are thus ill-suited to solving them. In this paper we present a hybrid analog/digital hardware architecture specifically designed to solve such problems. We cast CSPs as networks of stereotyped multi-stable oscillatory elements that communicate using digital pulses, or events. The oscillatory elements are implemented using analog non-stochastic circuits. The non-repeating phase relations among the oscillatory elements drive the exploration of the solution space. We show that this hardware architecture can yield state-of-the-art performance on a number of CSPs under reasonable assumptions on the implementation. We present measurements from a prototype electronic chip to demonstrate that a physical implementation of the proposed architecture is robust to practical non-idealities and to validate the theory proposed.Comment: First two authors contributed equally to this wor

On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing

In this paper, a chip that performs real-time image convolutions with programmable kernels of arbitrary shape is presented. The chip is a first experimental prototype of reduced size to validate the implemented circuits and system level techniques. The convolution processing is based on the address–event-representation (AER) technique, which is a spike-based biologically inspired image and video representation technique that favors communication bandwidth for pixels with more information. As a first test prototype, a pixel array of 16x16 has been implemented with programmable kernel size of up to 16x16. The chip has been fabricated in a standard 0.35- m complimentary metal–oxide–semiconductor (CMOS) process. The technique also allows to process larger size images by assembling 2-D arrays of such chips. Pixel operation exploits low-power mixed analog–digital circuit techniques. Because of the low currents involved (down to nanoamperes or even picoamperes), an important amount of pixel area is devoted to mismatch calibration. The rest of the chip uses digital circuit techniques, both synchronous and asynchronous. The fabricated chip has been thoroughly tested, both at the pixel level and at the system level. Specific computer interfaces have been developed for generating AER streams from conventional computers and feeding them as inputs to the convolution chip, and for grabbing AER streams coming out of the convolution chip and storing and analyzing them on computers. Extensive experimental results are provided. At the end of this paper, we provide discussions and results on scaling up the approach for larger pixel arrays and multilayer cortical AER systems.Commission of the European Communities IST-2001-34124 (CAVIAR)Commission of the European Communities 216777 (NABAB)Ministerio de Educación y Ciencia TIC-2000-0406-P4Ministerio de Educación y Ciencia TIC-2003-08164-C03-01Ministerio de Educación y Ciencia TEC2006-11730-C03-01Junta de Andalucía TIC-141

New virtually scaling free adaptive CORDIC rotator

In this article we propose a novel CORDIC rotator algorithm that eliminates the problems of scale factor compensation and limited range of convergence associated with the classical CORDIC algorithm. In our scheme, depending on the target angle or the initial coordinate of the vector, a scaling by 1 or 1/?2 is needed that can be realised with minimal hardware. The proposed CORDIC rotator adaptively selects appropriate iteration steps and converges to the final result by executing 50% less number of iterations on an average compared to that required for the classical CORDIC. Unlike classical CORDIC, the final value of the scale factor is completely independent of number of executed iterations. Based on the proposed algorithm, a 16-bit pipelined CORDIC rotator implementation has been described. The silicon area of the fabricated pipelined CORDIC rotator core is 2.73 mm2. This is equivalent to 38 k inverter gates in IHP in-house 0.25 ?m BiCMOS technology. The average dynamic power consumption of the fabricated CORDIC rotator is 17 mW @ 2.5 V supply and 20Msps throughput. Currently, this CORDIC rotator is used as a part of the baseband processor for a project that aims to design a single-chip wireless modem compliant with IEEE 802.11a and Hiperlan/2