Associative memory in computer architecture

3 credits The course outline below was developed as part of a statewide standardization process. General Course Purpose CSC 205 or CSC 215 is intended to fulfill a first course in Computer Architecture, Organization and Systems in the CS curriculum. The focus of CSC 215 is on Systems with a sampling of Architecture and Organization content. Course Prerequisites/Corequisites Prerequisite: Course Objectives • Machine level data representations • Describe how numbers, text, and other analog or discrete information can be represented in digital form • Interpret computer data representation of unsigned integer, signed integer (in 2's complement form) and floating-point values in the IEEE-754 formats • Explain the impact due to the limitations of data representations such as rounding effects and their propagation affect the accuracy of chained calculations, overflow errors, and mapping of continuous information to discrete representation • CPU and Instruction Set Architecture • Differentiate various instruction set architectures • Memory Hierarchy • Identify the memory technologies found in a computer processor and computing systems • Describe the various ways of organizing memory and the impacts on cost-performance tradeoffs, speed, capacity, latency, and volatility (also include long term storage with tape drives, hard drives, and SSDs with performance enhancements like RAID) • Describe the operation of common cache mapping schemes: Direct, Associative, and Set associative • D...

Associative Memory An associative memory can be considered as a memory unit whose stored data can be identified for access by the content of the data itself rather than by an address or memory location. Associative memory is often referred to as Content Addressable Memory (CAM). When a write operation is performed on associative memory, no address or memory location is given to the word. The memory itself is capable of finding an empty unused location to store the word. On the other hand, when the word is to be read from an associative memory, the content of the word, or part of the word, is specified. The words which match the specified content are located by the memory and are marked for reading. The following diagram shows the block representation of an Associative memory. From the block diagram, we can say that an associative memory consists of a memory array and logic for 'm' words with 'n' bits per word. The functional registers like the argument register A and key register K each have n bits, one for each bit of a word. The match register M consists of m bits, one for each memory word. The words which are kept in the memory are compared in parallel with the content of the argument register. The key register (K) provides a mask for choosing a particular field or key in the argument word. If the key register contains a binary value of all 1's, then the entire argument is compared with each memory word. Otherwise, only those bits in the argument that have 1's in their ...

Memory Systems Sarah L. Harris, David Harris, in Digital Design and Computer Architecture, 2022 Multiway Set Associative Cache An N-way set associative cache reduces conflicts by providing N blocks in each set where data mapping to that set might be found. Each memory address still maps to a specific set, but it can map to any one of the N blocks in the set. Hence, a direct mapped cache is another name for a one-way set associative cache. N is also called the degree of associativity of the cache. Figure 8.9 shows the hardware for a C = 8-word, N = 2-way set associative cache. The cache now has only S = 4 sets rather than 8. Thus, only log 24 = 2 set bits rather than 3 are used to select the set. The tag increases from 27 to 28 bits. Each set contains two ways or degrees of associativity. Each way consists of a data block and the valid and tag bits. The cache reads blocks from both ways in the selected set and checks the tags and valid bits for a hit. If a hit occurs in one of the ways, a multiplexer selects data from that way. Set associative caches generally have lower miss rates than direct mapped caches of the same capacity because they have fewer conflicts. However, set associative caches are usually slower and somewhat more expensive to build because of the output multiplexer and additional comparators. They also raise the question of which way to replace when both ways are full; this is addressed further in Section 8.3.3. Most commercial systems use set associative c...

Quantum Pattern Recognition Peter Wittek, in Quantum Machine Learning, 2014 11.5 Computational Complexity Quantum associative memory has a linear time complexity in the number of elements to be folded in the superposition and the number of dimensions ( O( Nd); Ventura and Martinez, 2000). Assuming that N ≫ d, this means that computational complexity is improved by a factor of N compared with the classical Hopfield network. Quantum artificial neural networks are most useful where the training set is large ( Narayanan and Menneer, 2000). Since the rate of convergence of the gradient descent in a classical neural network is not established for the general case, it is hard to estimate the overall improvement in the quantum case. The generic improvement with Grover’s algorithm is quadratic, and we can assume this for the quantum components of a quantum neural network. Read more Memory Models: Quantitative B. Murdock, in International Encyclopedia of the Social & Behavioral Sciences, 2001 3.2SAM Model This ‘search of associative memory’ model (Shiffrin and Raaijmakers 1992) is an updated version of the earlier and very influential Atkinson and Shiffrin buffer model (see Izawa 1999). Both models focus on the importance of a short-term store or rehearsal buffer. This has a very limited capacity (generally four items or two pairs) which serves as the antechamber to a much more capacious long-term memory. Although, again, there are no representation assumptions in SAM, the strength ...

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These kinds of neural networks work on the basis of pattern association, which means they can store different patterns and at the time of giving an output they can produce one of the stored patterns by matching them with the given input pattern. These types of memories are also called Content-Addressable Memory (CAM). Associative memory makes a parallel search with the stored patterns as data files. Following are the two types of associative memories we can observe − • Auto Associative Memory • Hetero Associative memory Auto Associative Memory This is a single layer neural network in which the input training vector and the output target vectors are the same. The weights are determined so that the network stores a set of patterns. Architecture As shown in the following figure, the architecture of Auto Associative memory network has ‘n’ number of input training vectors and similar ‘n’ number of output target vectors. Training Algorithm For training, this network is using the Hebb or Delta learning rule. Step 1− Initialize all the weights to zero as w ij = 0 (i = 1 to n, j = 1 to n) Step 2− Perform steps 3-4 for each input vector. Step 3− Activate each input unit as follows − $$x_$$

Among several emerging architectures, computing in memory (CIM), which features in-situ analog computation, is a potential solution to the data movement bottleneck of the Von Neumann architecture for artificial intelligence (AI). Interestingly, more strengths of CIM significantly different from in-situ analog computation are not widely known yet. In this work, we point out that mutually stationary vectors (MSVs), which can be maximized by introducing associativity to CIM, are another inherent power unique to CIM. By MSVs, CIM exhibits significant freedom to dynamically vectorize the stored data (e.g., weights) to perform agile computation using the dynamically formed vectors. We have designed and realized an SA-CIM silicon prototype and corresponding architecture and acceleration schemes in the TSMC 28 nm process. More specifically, the contributions of this paper are fourfold: 1) We identify MSVs as new features that can be exploited to improve the current performance and energy challenges of the CIM-based hardware. 2) We propose SA-CIM to enhance MSVs for skipping the zeros, small values, and sparse vectors. 3) We propose a transposed systolic dataflow to efficiently conduct conv3×3 while being capable of exploiting input-skipping schemes. 4) We propose a design flow to search for optimal aggressive skipping scheme setups while satisfying the accuracy loss constraint. The proposed ISSA architecture improves the throughput by 1.91× to 2.97× speedup and the energy efficien...

The objectives of this module are to discuss the concept of virtual memory and discuss the various implementations of virtual memory. All of us are aware of the fact that our program needs to be available in main memory for the processor to execute it. Assume that your computer has something like 32 or 64 MB RAM available for the CPU to use. Unfortunately, that amount of RAM is not enough to run all of the programs that most users expect to run at once. For example, if you load the operating system, an e-mail program, a Web browser and word processor into RAM simultaneously, 32 MB is not enough to hold all of them. If there were no such thing as virtual memory, then you will not be able to run your programs, unless some program is closed. With virtual memory, we do not view the program as one single piece. We divide it into pieces, and only the one part that is currently being referenced by the processor need to be available in main memory. The entire program is available in the hard disk. As the copying between the hard disk and main memory happens automatically, you don’t even know it is happening, and it makes your computer feel like is has unlimited RAM space even though it only has 32 MB installed. Because hard disk space is so much cheaper than RAM chips, it also has a n economic benefit. Techniques that automatically move program and data blocks into the physical main memory when they are required for execution are called virtual-memory techniques. Programs, and hen...