Programmable Computing Research Articles

Design and Application of Programmable Analog Computing Circuit for Kalman Filter Algorithm Based on Memristive Array

Design and Application of Programmable Analog Computing Circuit for Kalman Filter Algorithm Based on Memristive Array

End-to-end programmable computing systems

Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision.

Programmable Biomolecule-Mediated Processors.

Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous biomolecular structures as information-storing and -processing substrates to tackle computational problems. It is of great significance to investigate the various issues of programmable biomolecule-mediated processors that are capable of automatically processing, storing, and displaying information. This Perspective provides several conceptual designs of programmable biomolecule-mediated processors and provides some insights into potential future research directions for programmable biomolecule-mediated processors.

Performing photonic nonlinear computations by linear operations in a high-dimensional space.

As photonic linear computations are diverse and easy to realize while photonic nonlinear computations are relatively limited and difficult, we propose a novel way to perform photonic nonlinear computations by linear operations in a high-dimensional space, which can achieve many nonlinear functions different from existing optical methods. As a practical application, the arbitrary binary nonlinear computations between two Boolean signals are demonstrated to implement a programmable logic array. In the experiment, by programming the high-dimensional photonic matrix multiplier, we execute fourteen different logic operations with only one fixed nonlinear operation. Then the combined logic functions of half-adder and comparator are demonstrated at 10 Gbit/s. Compared with current methods, the proposed scheme simplifies the devices and the nonlinear operations for programmable logic computing. More importantly, nonlinear realization assisted by space transformation offers a new solution for optical digital computing and enriches the diversity of photonic nonlinear computing.

An interconnect-free micro-electromechanical 7-bit arithmetic device for multi-operand programmable computing

Computational power density and interconnection between transistors have grown to be the dominant challenges for the continued scaling of complementary metal–oxide–semiconductor (CMOS) technology due to limited integration density and computing power. Herein, we designed a novel, hardware-efficient, interconnect-free microelectromechanical 7:3 compressor using three microbeam resonators. Each resonator is configured with seven equal-weighted inputs and multiple driven frequencies, thus defining the transformation rules for transmitting resonance frequency to binary outputs, performing summation operations, and displaying outputs in compact binary format. The device achieves low power consumption and excellent switching reliability even after 3 × 103 repeated cycles. These performance improvements, including enhanced computational power capacity and hardware efficiency, are paramount for moderately downscaling devices. Finally, our proposed paradigm shift for circuit design provides an attractive alternative to traditional electronic digital computing and paves the way for multioperand programmable computing based on electromechanical systems.

Controllable DNA nanodevices regulated by logic gates for multi-stimulus recognition.

DNA biosensors have attracted considerable attention due to their great potential in environmental monitoring and medical diagnosis. Despite the great achievements, the single function and uncontrollability of the sensors restrict their further application. Therefore, it is necessary to construct controllable nanodevices with both sensing and responding capabilities to external stimuli. Herein, we develop a strategy to engineer structure-switching biosensors which can respond to external stimuli while preserving the sensing capability. The engineered nanodevice consists of an actuation module and a sensing module. Initially, the sensing module is disabled by a blocker strand which acts as an allosteric switch. Once the stimuli-responsive actuation module displaces the blocker DNA, the sensing module is activated. Based on the strategy, the engineered nanodevice could recognize both the target and external stimuli. As a demonstration of this strategy, a controllable Hg2+ sensor was designed, in which a 'YES', 'AND', and 'OR' logic gate is employed as the actuation module respectively to facilitate recognition of oligonucleotide inputs. The modular nature of the proposed strategy makes it easily generalizable to other structure-switching sensors. As a demonstration of this, we successfully apply it to the ATP sensor. The proposed strategy has potential in the fields of programmable biosensing, disease diagnosis, DNA computing, and intelligent nanodevices.

A 4.4–75-TOPS/W 14-nm Programmable, Performance- and Precision-Tunable All-Digital Stochastic Computing Neural Network Inference Accelerator

We present the first programmable and precision-tunable Stochastic Computing (SC) neural network (NN) inference accelerator. The use of SC makes it possible to achieve multiply-accumulate (MAC) density of 38.4k MAC/mm2, enabling a level of spatial data reuse unachievable to conventional, fixed-point architectures. This extensive reuse amortizes the cost of SC conversion and reduces the number of memory accesses, which can otherwise consume significant energy and latency. Our accelerator is a stand-alone architecture, with a custom instruction set architecture (ISA), and support for end-to-end model inference with convolutional and fully-connected layers of variable input and filter sizes. Further, it demonstrates extensive accuracy-latency trade-offs by varying the stream length. The 14nm demonstration chip achieves 2.4 TOPS and 75 TOPS/W peak throughput and energy efficiency, outperforming comparable fixed-point accelerators.

Optical and electrical programmable computing energy use comparison.

Optical computing has been proposed as a replacement for electrical computing to reduce energy use of math intensive programmable applications like machine learning. Objective energy use comparison requires that data transfer is separated from computing and made constant, with only computing variable. Three operations compared in this manner are multiplication, addition and inner product. For each, it is found that energy use is dominated by data transfer, and that computing energy use is a small fraction of the total. Switching to optical from electrical programmable computing does not reduce energy use.

Exploration of Intercell Wireless Millimeter-Wave Communication in the Landscape of Intelligent Metasurfaces

Software-defined metasurfaces are electromagnetically ultra-thin, artificial components that can provide engineered and externally controllable functionalities. The control over these functionalities is enabled by the metasurface tunability, which is implemented by embedded electronic circuits that modify locally the surface resistance and reactance. Integrating controllers within the metasurface able them to intercommunicate and adaptively reconfigure, thus imparting a desired electromagnetic operation, opens the path towards the creation of an artificially intelligent (AI) fabric where each unit cell can have its own sensing, programmable computing, and actuation facilities. In this work we take a crucial step towards bringing the AI metasurface technology to emerging applications, in particular exploring the wireless mm-wave intercell communication capabilities in a software-defined HyperSurface designed for operation in the microwave regime. We examine three different wireless communication channels within the landscape of the reflective metasurface: Firstly, in the layer where the control electronics of the HyperSurface lie, secondly inside a dedicated layer enclosed between two metallic plates, and, thirdly, inside the metasurface itself. For each case we examine the physical implementation of the mm-wave transceiver nodes, we quantify communication channel metrics, and we identify complexity vs. performance trade-offs.

Online Learning in a Chemical Perceptron

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and threshold. Both chemical perceptrons can successfully identify all 14 linearly separable two-input logic functions and maintain high robustness against rate-constant perturbations. We suggest that DNA strand displacement could, in principle, provide an implementation substrate for our model, allowing the chemical perceptron to perform reusable, programmable, and adaptable wet biochemical computing.

Performance Evaluation for Field Programmable Gate Array-based Bioinformatics Sequence Alignment

Sequence alignment is a computationally expensive activity in bioinformatics that can benefit significantly from Field Programmable Gate Array-based high-performance computing systems. This paper evaluates the performance of such systems for bioinformatics sequence alignment applications considering various parameters like data bus width (W), the number of processing elements, and the maximum operating frequency.

Hot-wiring cells

Hot-wiring cells

4th Generation Programmable Logic Computing: A Road Map

New and emerging programmable computing have the full potential to enormously boost the efficiency and flexibility with which programmable logics are used to support programmability and reusability of the systems. New systems implementations based on programmable computing offer the functional efficiency of hardware and the programmability of software, are quickly maturing as the logic capacity of programmable devices follows Moore’s Law and advanced automated design techniques become available.This paper presents an overview of the evolution of programmable logic computing devices. This paper also presents a survey of commercial development in programmable logic computing devices over the past twenty years. This includes an overview of the chip and system architectures of reprogrammable systems, as well as the applications of these systems.

Autonomous forward inference via DNA computing

Abstract Recent studies direct the research into building DNA computing machines with intelligence, which is measured by three main points: autonomous, programmable and able to learn and adapt. Logical inference plays and important role in programmable information processing or computing. Here we present a new method to perform autonomous molecular forward inference for expert system. A novel repetitive recognition site (RRS) technique is invented to design rule-molecules in knowledge base. The inference engine runs autonomously by digesting the rule-molecule, using a class IIB restriction enzyme Ppil. Concentration model has been built to show the feasibility of the inference process under ideal chemical reaction conditions. Moreover, we extend to implement a triggering communication between molecular automata, as a further application of the RRS technique in our model.

An analytical model for information retrieval in wireless sensor networks using enhanced apteen protocol

Wireless sensor networks are a new class of ad hoc networks that will find increasing deployment in coming years, as they enable reliable monitoring and analysis of unfamiliar and untested environments. The advances in technology have made it possible to have extremely small, low powered sensor devices equipped with programmable computing, multiple parameter sensing, and wireless communication capability. Because of their inherent limitations, the protocols designed for such sensor networks must efficiently use both limited bandwidth and battery energy. We develop an M/G/1 model to analytically determine the delay incurred in handling various types of queries using our enhanced APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network protocol) protocol. Our protocol uses an enhanced TDMA schedule to efficiently incorporate query handling, with a queuing mechanism for heavy loads. It also provides the additional flexibility of querying the network through any node in the network. To verify our analytical results, we have simulated a temperature sensing application with a Poisson arrival rate for queries on the network simulator ns-2. As the simulation and analytical results match perfectly well, this can be said to be the first step towards analytically determining the delay characteristics of a wireless sensor network.

A hybrid architecture for programmable computing and evolutionary learning

The relation between the structure and the computational function of the brain must have a plastic quality in order for it to have developed through the Darwinian mechanism of variation and selection. It is likely that such structure-function plasticity plays a role in ontogenetic learning as well. Present-day digital computers, by contrast, are highly programmable. But this powerful property entails rigidly defined structure-function relations that are generally incompatible with self-organization through evolution. Hybrid systems could combine the advantages of both programmability and evolvability. The system described in this paper comprises a conventional machine that communicates with an evolution-amenable cellular automaton system. The latter learns to use its low-level parallelism through a “copy thy neighbor” evolutionary algorithm. We describe some simulation experiments with the system and consider how direct fabrication might be achieved using silicon as well as nonsilicon technologies.