Abstract: A redundancy scheme for Non-Volatile Memories (NVM) is described. This redundancy scheme provides means for using defective cells in non-volatile memories to increase yield. The algorithm is based on inverting the program data for data being programmed to a cell grouping when a defective cell is detected in the cell grouping. Defective cells are biased to either “1” or “0” logic states, which are effectively preset to store its biased logic state. A data bit to be stored in a defective cell having a logic state that is complementary to the biased logic state of the cell results in the program data being inverted and programmed. An inversion status bit is programmed to indicate the inverted status of the programmed data. During read out, the inversion status bit causes the stored data to be re-inverted into its original program data states.

Abstract: A redundancy scheme for Non-Volatile Memories (NVM) is described. This redundancy scheme provides means for using defective cells in non-volatile memories to increase yield. The algorithm is based on inverting the program data for data being programmed to a cell grouping when a defective cell is detected in the cell grouping. Defective cells are biased to either “1” or “0” logic states, which are effectively preset to store its biased logic state. A data bit to be stored in a defective cell having a logic state that is complementary to the biased logic state of the cell results in the program data being inverted and programmed. An inversion status bit is programmed to indicate the inverted status of the programmed data. During read out, the inversion status bit causes the stored data to be re-inverted into its original program data states.

Abstract: A redundancy scheme for Non-Volatile Memories (NVM) is described. This redundancy scheme provides means for using defective cells in non-volatile memories to increase yield. The algorithm is based on inverting the program data for data being programmed to a cell grouping when a defective cell is detected in the cell grouping. Defective cells are biased to either “1” or “0” logic states, which are effectively preset to store its biased logic state. A data bit to be stored in a defective cell having a logic state that is complementary to the biased logic state of the cell results in the program data being inverted and programmed. An inversion status bit is programmed to indicate the inverted status of the programmed data. During read out, the inversion status bit causes the stored data to be re-inverted into its original program data states.

Abstract: A redundancy scheme for Non-Volatile Memories (NVM) is described. This redundancy scheme provides means for using defective cells in non-volatile memories to increase yield. The algorithm is based on inverting the program data for data being programmed to a cell grouping when a defective cell is detected in the cell grouping. Defective cells are biased to either “1” or “0” logic states, which are effectively preset to store its biased logic state. A data bit to be stored in a defective cell having a logic state that is complementary to the biased logic state of the cell results in the program data being inverted and programmed. An inversion status bit is programmed to indicate the inverted status of the programmed data. During read out, the inversion status bit causes the stored data to be re-inverted into its original program data states.

Abstract: A redundancy scheme for Non-Volatile Memories (NVM) is described. This redundancy scheme provides means for using defective cells in non-volatile memories to increase yield. The algorithm is based on inverting the program data for data being programmed to a cell grouping when a defective cell is detected in the cell grouping. Defective cells are biased to either “1” or “0” logic states, which are effectively preset to store its biased logic state. A data bit to be stored in a defective cell having a logic state that is complementary to the biased logic state of the cell results in the program data being inverted and programmed. An inversion status bit is programmed to indicate the inverted status of the programmed data. During read out, the inversion status bit causes the stored data to be re-inverted into its original program data states.

Abstract: Method and apparatus for using a Content Addressable Memory for sorting a plurality of data items is presented. The data items to be sorted are stored in the Content Addressable Memory. A plurality of bit-by-bit burst searches are performed on the contents of the Content Addressable Memory with all other bits in the search key masked. The number of burst searches is proportional to the total number of bits in the data items to be sorted. The search is deterministic dependent on the number of bits in each data item on which a sort is performed and on the number of data items to be sorted.

Abstract: Method and apparatus for using a Content Addressable Memory for sorting a plurality of data items is presented. The data items to be sorted are stored in the Content Addressable Memory. A plurality of bit-by-bit burst searches are performed on the contents of the Content Addressable Memory with all other bits in the search key masked. The number of burst searches is proportional to the total number of bits in the data items to be sorted. The search is deterministic dependent on the number of bits in each data item on which a sort is performed and on the number of data items to be sorted.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: Method and apparatus for using a Content Addressable Memory for sorting a plurality of data items is presented. The data items to be sorted are stored in the Content Addressable Memory. A plurality of bit-by-bit burst searches are performed on the contents of the Content Addressable Memory with all other bits in the search key masked. The number of burst searches is proportional to the total number of bits in the data items to be sorted. The search is deterministic dependent on the number of bits in each data item on which a sort is performed and on the number of data items to be sorted.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: Method and apparatus for using a Content Addressable Memory for sorting a plurality of data items is presented. The data items to be sorted are stored in the Content Addressable Memory. A plurality of bit-by-bit burst searches are performed on the contents of the Content Addressable Memory with all other bits in the search key masked. The number of burst searches is proportional to the total number of bits in the data items to be sorted. The search is deterministic dependent on the number of bits in each data item on which a sort is performed and on the number of data items to be sorted.

Abstract: Method and apparatus using a Content Addressable Memory for sorting a plurality of data items is presented. The data items to be sorted are stored in the Content Addressable Memory. A plurality of bit-by-bit burst searches are performed on the contents of the Content Addressable Memory with all other bits in the search key masked. The number of burst searches is proportional to the total number of bits in the data items to be sorted. The search is deterministic dependent on the number of bits in each data item on which a sort is performed and on the number of data items to be sorted.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: Method and apparatus using a Content Addressable Memory for sorting a plurality of data items is presented. The data items to be sorted are stored in the Content Addressable Memory. A plurality of bit-by-bit burst searches are performed on the contents of the Content Addressable Memory with all other bits in the search key masked. The number of burst searches is proportional to the total number of bits in the data items to be sorted. The search is deterministic dependent on the number of bits in each data item on which a sort is performed and on the number of data items to be sorted.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: New techniques for generating entries in a content addressable memory (CAM) capable of comparison operations such as “greater than” and “less than” decisions are described. The techniques can be used with binary or ternary CAMs. The number of CAM entries needed to implement such decisions is drastically reduced for both binary and ternary CAMs. In the case of binary CAMs, one or multiple searches are needed to perform the comparisons, while in the case of ternary CAMs a tradeoff between the number of CAM entries and the number of searches can be found. As an example, a method of classifying data networking packets is implemented using the new techniques. A packet classifier based on subfields of a packet header is also described.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: We present a method and apparatus for performing adaptive data compression. An alphabet and vocabulary in the encoder and decoder is built adaptively and stored in a dictionary as symbols are to be encoded and decoded. Each time an unknown symbol is to be encoded by the encoder, the encoder adds the symbol to the dictionary and transmits it in plain in the encoded string. The code words transmitted by the encoder include symbols and indexes. The state of a prefix bit preceding the code word indicates whether the code word is a plain symbol or an index of a symbol or string of symbols stored in the dictionary. The decoder examines the prefix bit of each code word as it is received to determine if the code word stores a symbol in plain or in index. If the code word stores a symbol in plain, the decoder learns the symbol by adding a sequence of symbols resulting from the concatenation of previously decoded symbols and the first symbol of the currently decoded symbol and by adding the symbol to its dictionary.

Abstract: New techniques for generating entries in a content addressable memory (CAM) capable of comparison operations such as “greater than” and “less than” decisions are described. The techniques can be used with binary or ternary CAMs. The number of CAM entries needed to implement such decisions is drastically reduced for both binary and ternary CAMs. In the case of binary CAMs, one or multiple searches are needed to perform the comparisons, while in the case of ternary CAMs a tradeoff between the number of CAM entries and the number of searches can be found. As an example, a method of classifying data networking packets is implemented using the new techniques. A packet classifier based on subfields of a packet header is also described.