Digital implementation of the cellular sensor-computers

Two different kinds of cellular sensor-processor architectures are used nowadays in various applications. The first is the traditional sensor-processor architecture, where the sensor and the processor arrays are mapped into each other. The second is the foveal architecture, in which a small active fovea is navigating in a large sensor array. This second architecture is introduced and compared here. Both of these architectures can be implemented with analog and digital processor arrays. The efficiency of the different implementation types, depending on the used CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use digital implementation rather than analog

Configurable 3D-integrated focal-plane sensor-processor array architecture

A mixed-signal Cellular Visual Microprocessor architecture with digital processors is described. An ASIC implementation is also demonstrated. The architecture is composed of a regular sensor readout circuit array, prepared for 3D face-to-face type integration, and one or several cascaded array of mainly identical (SIMD) processing elements. The individual array elements derived from the same general HDL description and could be of different in size, aspect ratio, and computing resources

Research in the effective implementation of guidance computers with large scale arrays Interim report

Functional logic character implementation in breadboard design of NASA modular compute

The use of field-programmable gate arrays for the hardware acceleration of design automation tasks

This paper investigates the possibility of using Field-Programmable Gate Arrays (Fr’GAS) as reconfigurable co-processors for workstations to produce moderate speedups for most tasks in the design process, resulting in a worthwhile overall design process speedup at low cost and allowing algorithm upgrades with no hardware modification. The use of FPGAS as hardware accelerators is reviewed and then achievable speedups are predicted for logic simulation and VLSI design rule checking tasks for various FPGA co-processor arrangements

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Quantum Dot Cellular Automata Check Node Implementation for LDPC Decoders

The quantum dot Cellular Automata (QCA) is an emerging nanotechnology that has gained significant research interest in recent years. Extremely small feature sizes, ultralow power consumption, and high clock frequency make QCA a potentially attractive solution for implementing computing architectures at the nanoscale. To be considered as a suitable CMOS substitute, the QCA technology must be able to implement complex real-time applications with affordable complexity. Low density parity check (LDPC) decoding is one of such applications. The core of LDPC decoding lies in the check node (CN) processing element which executes actual decoding algorithm and contributes toward overall performance and complexity of the LDPC decoder. This study presents a novel QCA architecture for partial parallel, layered LDPC check node. The CN executes Normalized Min Sum decoding algorithm and is flexible to support CN degree dc up to 20. The CN is constructed using a VHDL behavioral model of QCA elementary circuits which provides a hierarchical bottom up approach to evaluate the logical behavior, area, and power dissipation of the whole design. Performance evaluations are reported for the two main implementations of QCA i.e. molecular and magneti