To validate the efficiency of our co-optimization methodology, we evaluated the proposed method on three benchmarks, language modeling, speech recognition and image classification.
#Betterzip huffman cod software#
Along with the software approach, we also present a hardware architecture for processing sparse GEMM operations to maximize the benefit of the proposed pruning algorithm and sparse matrix format. As the benefit of the proposed pruning algorithm may be limited by the sparsity level of a given weight matrix, we present additional steps to further improve its efficiency.
First, we present an automated pruning algorithm, named AutoRelax, that allows some level of relaxation to achieve higher compression ratio. Since efficient priority queue data structures require O(log(n)) time per insertion, and a complete binary tree with n leaves has 2n-1 nodes, and Huffman coding tree is a complete binary tree, this algorithm operates in O(n.log(n)) time, where n is the total number of characters.ĭr.We propose a HW-SW co-optimization technique to perform energy-efficient spGEMM operations for deep learning. To make the program readable, we have used string class to store the above program’s encoded string. Note that the input string’s storage is 47×8 = 376 bits, but our encoded string only takes 194 bits, i.e., about 48% of data compression. The decoded string is: Huffman coding is a data compression algorithm. The original string is: Huffman coding is a data compression algorithm. We will use a priority queue for building Huffman Tree, where the node with the lowest frequency has the highest priority.
#Betterzip huffman cod code#
It is recommended that Huffman Tree should discard unused characters in the text to produce the most optimal code lengths. A finished tree has n leaf nodes and n-1 internal nodes. As a common convention, bit 0 represents following the left child, and a bit 1 represents following the right child. Internal nodes contain character weight and links to two child nodes. Initially, all nodes are leaf nodes, which contain the character itself, the weight (frequency of appearance) of the character. A node can be either a leaf node or an internal node. The technique works by creating a binary tree of nodes. Now that we are clear on variable-length encoding and prefix rule, let’s talk about Huffman coding. Now we can uniquely decode 00100110111010 back to our original string aabacdab. We start by randomly assigning a single bit code 0 to a, 2–bit code 11 to b, and 3–bit code 100 and 011 to characters c and d, respectively. Let’s try to represent aabacdab using a lesser number of bits by using the fact that a occurs more frequently than b, and b occurs more frequently than c and d. If we note, the frequency of characters a, b, c and d are 4, 2, 1, 1, respectively. It has 8 characters in it and uses 64–bits storage (using fixed-length encoding).
#Betterzip huffman cod how to#
Given a sequence of bits, how to decode it uniquely? The problem with variable-length encoding lies in its decoding. So, some characters might end up taking a single bit, and some might end up taking two bits, some might be encoded using three bits, and so on. In variable-length encoding, we assign a variable number of bits to characters depending upon their frequency in the given text. We can exploit the fact that some characters occur more frequently than others in a text (refer to this) to design an algorithm that can represent the same piece of text using a lesser number of bits. The idea is to use “variable-length encoding”. Given a text, how to reduce the amount of space required to store a character? This is known as “fixed-length encoding”, as each character uses the same number of fixed-bit storage. We already know that every character is sequences of 0's and 1's and stored using 8-bits. This post talks about the fixed-length and variable-length encoding, uniquely decodable codes, prefix rules, and Huffman Tree construction. Huffman coding (also known as Huffman Encoding) is an algorithm for doing data compression, and it forms the basic idea behind file compression.
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