Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many matrix multiplication- dependent applications can use reduced-precision integer or fixed- point representations to increase their performance and energy efficiency while still offering adequate quality of results. However, precision requirements may vary between different application phases or depend on input data, rendering constant-precision solutions ineffective. We present BISMO, a vectorized bit- serial matrix multiplication overlay for reconfigurable computing. BISMO utilizes the excellent binary-operation performance o...
The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra l...
Floating-point matrix multiplication is a basic kernel in scientific computing. It has been shown th...
Computations involving matrices form the kernel of a large spectrum of computationally demanding app...
Matrix-matrix multiplication is a key computational kernel for numerous applications in science and ...
We present two designs (I and II) for IEEE 754 double precision floating point matrix multiplication...
Matrix multiplication is required for a wide variety of applications, including data mining, linear ...
Abstract — In this paper, we introduce a scalable macro-pipelined architecture to perform floating p...
To solve the computational complexity and time-consuming problem of large matrix multiplication, thi...
We introduce a 64-bit ANSI/IEEE Std 754-1985 floating point design of a hardware matrix multiplier o...
Matrix operations, like matrix multiplication, are commonly used in almost all areas of scientific r...
Part 4: Architecture and HardwareInternational audienceMatrix computing plays a vital role in many s...
Matrix multiplication is at the core of high-performance numerical computation. Software methods of ...
Abstract—Energy efficiency has emerged as one of the key performance metrics in computing. In this w...
ABSTRACT: In this paper, we have proposed one designs for matrix-matrix multiplication. The one desi...
Masters Research - Master of Philosophy (MPhil)Matrix-vector multiplication is widely used in scienc...
The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra l...
Floating-point matrix multiplication is a basic kernel in scientific computing. It has been shown th...
Computations involving matrices form the kernel of a large spectrum of computationally demanding app...
Matrix-matrix multiplication is a key computational kernel for numerous applications in science and ...
We present two designs (I and II) for IEEE 754 double precision floating point matrix multiplication...
Matrix multiplication is required for a wide variety of applications, including data mining, linear ...
Abstract — In this paper, we introduce a scalable macro-pipelined architecture to perform floating p...
To solve the computational complexity and time-consuming problem of large matrix multiplication, thi...
We introduce a 64-bit ANSI/IEEE Std 754-1985 floating point design of a hardware matrix multiplier o...
Matrix operations, like matrix multiplication, are commonly used in almost all areas of scientific r...
Part 4: Architecture and HardwareInternational audienceMatrix computing plays a vital role in many s...
Matrix multiplication is at the core of high-performance numerical computation. Software methods of ...
Abstract—Energy efficiency has emerged as one of the key performance metrics in computing. In this w...
ABSTRACT: In this paper, we have proposed one designs for matrix-matrix multiplication. The one desi...
Masters Research - Master of Philosophy (MPhil)Matrix-vector multiplication is widely used in scienc...
The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra l...
Floating-point matrix multiplication is a basic kernel in scientific computing. It has been shown th...
Computations involving matrices form the kernel of a large spectrum of computationally demanding app...