Memories for Life: A Review of the Science and Technology

This paper discusses scientific, social and technological aspects of memory. Recent developments in our understanding of memory processes and mechanisms, and their digital implementation, have placed the encoding, storage, management and retrieval of information at the forefront of several fields of research. At the same time, the divisions between the biological, physical and the digital worlds seem to be dissolving. Hence opportunities for interdisciplinary research into memory are being created, between the life sciences, social sciences and physical sciences. Such research may benefit from immediate application into information management technology as a testbed. The paper describes one initiative, Memories for Life, as a potential common problem space for the various interested disciplines

Integer Sparse Distributed Memory and Modular Composite Representation

Challenging AI applications, such as cognitive architectures, natural language understanding, and visual object recognition share some basic operations including pattern recognition, sequence learning, clustering, and association of related data. Both the representations used and the structure of a system significantly influence which tasks and problems are most readily supported. A memory model and a representation that facilitate these basic tasks would greatly improve the performance of these challenging AI applications.Sparse Distributed Memory (SDM), based on large binary vectors, has several desirable properties: auto-associativity, content addressability, distributed storage, robustness over noisy inputs that would facilitate the implementation of challenging AI applications. Here I introduce two variations on the original SDM, the Extended SDM and the Integer SDM, that significantly improve these desirable properties, as well as a new form of reduced description representation named MCR.Extended SDM, which uses word vectors of larger size than address vectors, enhances its hetero-associativity, improving the storage of sequences of vectors, as well as of other data structures. A novel sequence learning mechanism is introduced, and several experiments demonstrate the capacity and sequence learning capability of this memory.Integer SDM uses modular integer vectors rather than binary vectors, improving the representation capabilities of the memory and its noise robustness. Several experiments show its capacity and noise robustness. Theoretical analyses of its capacity and fidelity are also presented.A reduced description represents a whole hierarchy using a single high-dimensional vector, which can recover individual items and directly be used for complex calculations and procedures, such as making analogies. Furthermore, the hierarchy can be reconstructed from the single vector. Modular Composite Representation (MCR), a new reduced description model for the representation used in challenging AI applications, provides an attractive tradeoff between expressiveness and simplicity of operations. A theoretical analysis of its noise robustness, several experiments, and comparisons with similar models are presented.My implementations of these memories include an object oriented version using a RAM cache, a version for distributed and multi-threading execution, and a GPU version for fast vector processing

Multimedia terminal system-on-chip design and simulation

This paper proposes a design approach based on integrated architectural and system-on-chip (SoC) simulations. The main idea is to have an efficient framework for the design and the evaluation of multimedia terminals, allowing a fast system simulation with a definable degree of accuracy. The design approach includes the simulation of very long instruction word (VLIW) digital signal processors (DSPs), the utilization of a device multiplexing the media streams, and the emulation of the real-time media acquisition. This methodology allows the evaluation of both the multimedia algorithm implementations and the hardware platform, giving feedback on the complete SoC including the interaction between modules and conflicts in accessing either the bus or shared resources. An instruction set architecture (ISA) simulator and an SoC simulation environment compose the integrated framework. In order to validate this approach, the evaluation of an audio-video multiprocessor terminal is presented, and the complete simulation test results are reported

Source Memory Revealed Through Eye Movements and Pupil Dilation

abstract: Current theoretical debate, crossing the bounds of memory theory and mental imagery, surrounds the role of eye movements in successful encoding and retrieval. Although the eyes have been shown to revisit previously-viewed locations during retrieval, the functional role of these saccades is not known. Understanding the potential role of eye movements may help address classic questions in recognition memory. Specifically, are episodic traces rich and detailed, characterized by a single strength-driven recognition process, or are they better described by two separate processes, one for vague information and one for the retrieval of detail? Three experiments are reported, in which participants encoded audio-visual information while completing controlled patterns of eye movements. By presenting information in four sources (i.e., voices), assessments of specific and partial source memory were measured at retrieval. Across experiments, participants' eye movements at test were manipulated. Experiment 1 allowed free viewing, Experiment 2 required externally-cued fixations to previously-relevant (or irrelevant) screen locations, and Experiment 3 required externally-cued new or familiar oculomotor patterns to multiple screen locations in succession. Although eye movements were spontaneously reinstated when gaze was unconstrained during retrieval (Experiment 1), externally-cueing participants to re-engage in fixations or oculomotor patterns from encoding (Experiments 2 and 3) did not enhance retrieval. Across all experiments, participants' memories were well-described by signal-detection models of memory. Source retrieval was characterized by a continuous process, with evidence that source retrieval occurred following item memory failures, and additional evidence that participants partially recollected source, in the absence of specific item retrieval. Pupillometry provided an unbiased metric by which to compute receiver operating characteristic (ROC) curves, which were consistently curvilinear (but linear in z-space), supporting signal-detection predictions over those from dual-process theories. Implications for theoretical views of memory representations are discussed.Dissertation/ThesisPh.D. Psychology 201

Deep in-memory computing

There is much interest in embedding data analytics into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and Internet-of-Things to provide these with decision-making capabilities. Such platforms often need to implement machine learning (ML) algorithms under stringent energy constraints with battery-powered electronics. Especially, energy consumption in memory subsystems dominates such a system's energy efficiency. In addition, the memory access latency is a major bottleneck for overall system throughput. To address these issues in memory-intensive inference applications, this dissertation proposes deep in-memory accelerator (DIMA), which deeply embeds computation into the memory array, employing two key principles: (1) accessing and processing multiple rows of memory array at a time, and (2) embedding pitch-matched low-swing analog processing at the periphery of bitcell array. The signal-to-noise ratio (SNR) is budgeted by employing low-swing operations in both memory read and processing to exploit the application level's error immunity for aggressive energy efficiency. This dissertation first describes the system rationale underlying the DIMA's processing stages by identifying the common functional flow across a diverse set of inference algorithms. Based on the analysis, this dissertation presents a multi-functional DIMA to support four algorithms: support vector machine (SVM), template matching (TM), k-nearest neighbor (k-NN), and matched filter. The circuit and architectural level design techniques and guidelines are provided to address the challenges in achieving multi-functionality. A prototype integrated circuit (IC) of a multi-functional DIMA was fabricated with a 16 KB SRAM array in a 65 nm CMOS process. Measurement results show up to 5.6X and 5.8X energy and delay reductions leading to 31X energy delay product (EDP) reduction with negligible (<1%) accuracy degradation as compared to the conventional 8-b fixed-point digital implementation optimally designed for each algorithm. Then, DIMA also has been applied to more complex algorithms: (1) convolutional neural network (CNN), (2) sparse distributed memory (SDM), and (3) random forest (RF). System-level simulations of CNN using circuit behavioral models in a 45 nm SOI CMOS demonstrate that high probability (>0.99) of handwritten digit recognition can be achieved using the MNIST database, along with a 24.5X reduced EDP, a 5.0X reduced energy, and a 4.9X higher throughput as compared to the conventional system. The DIMA-based SDM architecture also achieves up to 25X and 12X delay and energy reductions, respectively, over conventional SDM with negligible accuracy degradation (within 0.4%) for 16X16 binary-pixel image classification. A DIMA-based RF was realized as a prototype IC with a 16 KB SRAM array in a 65 nm process. To the best of our knowledge, this is the first IC realization of an RF algorithm. The measurement results show that the prototype achieves a 6.8X lower EDP compared to a conventional design at the same accuracy (94%) for an eight-class traffic sign recognition problem. The multi-functional DIMA and extension to other algorithms naturally motivated us to consider a programmable DIMA instruction set architecture (ISA), namely MATI. This dissertation explores a synergistic combination of the instruction set, architecture and circuit design to achieve the programmability without losing DIMA's energy and throughput benefits. Employing silicon-validated energy, delay and behavioral models of deep in-memory components, we demonstrate that MATI is able to realize nine ML benchmarks while incurring negligible overhead in energy (< 0.1%), and area (4.5%), and in throughput, over a fixed four-function DIMA. In this process, MATI is able to simultaneously achieve enhancements in both energy (2.5X to 5.5X) and throughput (1.4X to 3.4X) for an overall EDP improvement of up to 12.6X over fixed-function digital architectures

Center for Space Microelectronics Technology 1988-1989 technical report

The 1988 to 1989 Technical Report of the JPL Center for Space Microelectronics Technology summarizes the technical accomplishments, publications, presentations, and patents of the center. Listed are 321 publications, 282 presentations, and 140 new technology reports and patents

Hemodynamic responses in human multisensory and auditory association cortex to purely visual stimulation

BACKGROUND: Recent findings of a tight coupling between visual and auditory association cortices during multisensory perception in monkeys and humans raise the question whether consistent paired presentation of simple visual and auditory stimuli prompts conditioned responses in unimodal auditory regions or multimodal association cortex once visual stimuli are presented in isolation in a post-conditioning run. To address this issue fifteen healthy participants partook in a "silent" sparse temporal event-related fMRI study. In the first (visual control) habituation phase they were presented with briefly red flashing visual stimuli. In the second (auditory control) habituation phase they heard brief telephone ringing. In the third (conditioning) phase we coincidently presented the visual stimulus (CS) paired with the auditory stimulus (UCS). In the fourth phase participants either viewed flashes paired with the auditory stimulus (maintenance, CS-) or viewed the visual stimulus in isolation (extinction, CS+) according to a 5:10 partial reinforcement schedule. The participants had no other task than attending to the stimuli and indicating the end of each trial by pressing a button. RESULTS: During unpaired visual presentations (preceding and following the paired presentation) we observed significant brain responses beyond primary visual cortex in the bilateral posterior auditory association cortex (planum temporale, planum parietale) and in the right superior temporal sulcus whereas the primary auditory regions were not involved. By contrast, the activity in auditory core regions was markedly larger when participants were presented with auditory stimuli. CONCLUSION: These results demonstrate involvement of multisensory and auditory association areas in perception of unimodal visual stimulation which may reflect the instantaneous forming of multisensory associations and cannot be attributed to sensation of an auditory event. More importantly, we are able to show that brain responses in multisensory cortices do not necessarily emerge from associative learning but even occur spontaneously to simple visual stimulation

Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.Comment: Preprin